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10 Types of Software Testing Models

January 14th, 2024.

10 Types of Software Testing Models

Testing is an integral part of the software development life cycle. Various models or approaches are used in the software development process, and each model has its own advantages and disadvantages. Choosing a particular model depends on the project deliverables and the complexity of the project.

What Are Software Testing Models?

Software testing models are systematic approaches used to plan, design, execute, and manage testing activities. They provide guidelines for carrying out testing processes effectively and ensure comprehensive test coverage.

Each model offers distinct advantages and is chosen based on the specific requirements of the project and the organization’s preferences. Understanding these models is crucial for selecting the most suitable approach for software testing in a given scenario.

Now let us go through the various software testing models and their benefits:

#1. Waterfall Model

Waterfall Model

This is the most basic software development life cycle process, which is broadly followed in the industry. Here, the developers follow a sequence of processes where the processes flow progressively downward towards the ultimate goal. It is like a waterfall where there are a number of phases.

These phases each have their own unique functions and goals. There are, in fact, four phases: requirement gathering and analysis phase, software design, programmed implementation and testing, and maintenance. All these four phases come one after another in the given order.

In the first phase, all the possible system requirements for developing a particular software are noted and analyzed. This, in turn, depends on the software requirement specifications , which include detailed information about the expectations of the end user. Based on this, a requirement specification.

A document is created that acts as input to the next phase, i.e., the software design phase. What needs to be emphasized here is that once you move into the next phase, it won’t be possible to update the requirements. So you must be very thorough and careful about the end-user requirements.

  • Easy to implement and maintain.
  • The initial phase of rigorous scrutiny of requirements and systems helps save time later in the developmental phase
  • The requirement for resources is minimal, and testing is done after the completion of each phase.

Disadvantages

  • It is not possible to alter or update the requirements
  • You cannot make changes once you are in the next phase.
  • You cannot start the next phase until the previous phase is completed

#2. V Model

v model in software testing

This model is widely recognized as superior to the waterfall model. Here, the development and test execution activities are carried out side by side in a downhill and uphill shape. In this model, testing starts at the unit level and spreads toward integration of the entire system.

task testing models

So, SDLC is divided into five phases – unit testing , integration testing, regression testing , system testing, and acceptance testing.

  • It is easy to use the model since testing activities like planning and test design are done before coding
  • Saves time and enhances the chances of success.
  • Defects are mostly found at an early stage, and the downward flow of defects is generally avoided
  • It is a rigid model
  • Early prototypes of the product are not available since the software is developed during the implementation phase
  • If there are changes in the midway, then the test document needs to be updated

#3. Agile model

agile testing quadrants

In this SDLC model, requirements and solutions evolve through collaboration between various cross-functional teams. This is known as an iterative and incremental model.

Also Read:  Selenium Tutorial For Beginners- An Overall View into this Tool.

  • Ensure customer satisfaction with the rapid and continuous development of deliverables.
  • It is a flexible model as customers, developers, and testers continuously interact with each other
  • Working software can be developed quickly, and products can be adapted to changing requirements regularly
  • In large and complex software development cases, it becomes difficult to assess the effort required at the beginning of the cycle
  • Due to continuous interaction with the customer, the project can go off track if the customer is not clear about the goals

#4. Spiral model

spiral model diagram

It is more like the Agile model , but with more emphasis on risk analysis. It has four phases: planning, risk analysis, engineering, and evaluation. Here, the gathering of requirements and risk assessment is done at the base level, and every upper spiral builds on it.

  • Risk avoidance is enhanced due to the importance of risk analysis.
  • Its a good model for complex and large systems.
  • Depending on the changed circumstances, additional functionalities can be added later on
  • Software is produced early in the cycle
  • Its a costly model and requires highly specialized expertise in risk analysis
  • It does not work well in simpler projects

#5. Rational Unified Process

Rational Unified Process Methodology

This model also consists of four phases, each of which is organized into a number of separate iterations. The difference with other models is that each of these iterations must separately satisfy defined criteria before the next phase is undertaken.

  • With an emphasis on accurate documentation, this model is able to resolve risks associated with changing client requirements.
  • Integration takes less time as the process goes on throughout the SDLC.
  • The biggest disadvantage is that the team members must be experts in their niche .
  • In big projects such as continuous integration, it might give rise to confusion

#6. Rapid application development

This is another incremental model, like the Agile model. Here, the components are developed parallel to each other. The developments are then assembled into a product.

  • The development time is reduced due to the simultaneous development of components, and the components can be reused
  • A lot of integration issues are resolved due to integration from the initial stage

task testing models

  • It requires a strong team of highly capable developers with individual efficacy in identifying business requirements
  • It is a module-based model, so systems that can be modularized can only be developed in this model
  • As the cost is high, the model is not suitable for cheaper projects

#7 Iterative Model

The iterative model does not require a complete list of requirements before the start of the project. The development process begins with the functional requirements, which can be enhanced later. The procedure is cyclic and produces new versions of the software for each cycle. Every iteration develops a separate component in the system that adds to what has been preserved from earlier functions.

  • It is easier to manage the risks since high-risk tasks are performed first.
  • The progress is easily measurable.
  • Problems and risks that are labeled within one iteration can be avoided in subsequent sprints.
  • The iterative model needs more resources compared to the waterfall model.
  • Managing the process is difficult.
  • The final stage of the project may not entirely determine the risks.

#8 Kanban Model

The Kanban Model is a visual and flow-based approach to software development and project management. It relies on a visual board to represent work items, which move through different process stages. These stages include backlog, analysis, development, testing, and deployment.

Each work item in a Kanban system has a card on the board to represent it, and team members move these cards through the stages as they complete them.

The board provides a real-time visual representation of the work in progress and helps teams identify bottlenecks or areas for improvement.

Continuous improvement is a key principle of Kanban. Teams regularly review their processes, identify areas of inefficiency, and make incremental changes to enhance workflow. This adaptability and focus on improvement make the Kanban Model well-suited for projects with evolving requirements and a need for continuous delivery.

Advantages of Kanban Model:

  • Visual Representation: Provides a clear visual overview of work items and their progress.
  • Flexibility: It is adaptable to changing priorities and requirements, making it suitable for dynamic projects.
  • Continuous Improvement: Encourages regular process reviews and enhancements for increased efficiency.
  • Reduced Waste: Minimizes unnecessary work by focusing on completing tasks based on actual demand.

Disadvantages of the Kanban Model:

  • Limited Planning: Less emphasis on detailed planning may be a drawback for projects requiring extensive upfront planning.
  • Dependency on WIP Limits: Ineffective management of work-in-progress (WIP) limits can lead to bottlenecks.
  • Complexity Management: This may become complex for large-scale projects or those with intricate dependencies.
  • Team Dependency: This relies on team collaboration and communication, which can be challenging if not well coordinated.

#9 The Big Bang Model

  • No Formal Design or Planning: The Big Bang Model is characterized by an absence of detailed planning or formal design before the development process begins.
  • Random Testing Approach: Testing is conducted randomly, without a predefined strategy or specific testing phases.
  • Suitable for Small Projects: This model is often considered suitable for small-scale projects or projects with unclear requirements.

Advantages of the Big Bang Model:

  • Simplicity: The model is simple and easy to understand.
  • Quick Start: Quick initiation, as there is no need for elaborate planning.

Disadvantages of the Big Bang Model:

  • Uncertainty: Lack of planning and design can lead to uncertainty and chaos during development.
  • Testing Challenges: Random testing may result in inadequate test coverage, and missing critical issues.
  • Limited Scalability: Not suitable for large or complex projects due to a lack of structured processes.

#10 Scrum Model

  • Framework within Agile: Scrum is a framework operating within the Agile methodology, emphasizing iterative development and collaboration.
  • Sprints for Short Development Cycles: Development occurs in short, fixed intervals known as sprints, typically lasting 2-4 weeks.
  • Adaptability and Rapid Releases: Scrum promotes adaptability to changing requirements and aims for rapid, incremental releases.

Advantages of Scrum Model:

  • Flexibility: Allows for flexibility in responding to changing project requirements.
  • Customer Satisfaction: Regular deliverables enhance customer satisfaction and engagement.
  • Continuous Improvement: Emphasizes continuous improvement through regular retrospectives.

Disadvantages of the Scrum Model:

  • Lack of Structure: Some teams may struggle with flexibility and lack of a structured plan.
  • Dependency on Team Collaboration: Success heavily depends on effective collaboration within the development team.
  • Limited Predictability: It may be challenging to predict the exact outcomes and timeline due to the iterative nature.

The future of software development models

Software application testing is an area that is changing fast with the evolution of new technologies and higher user expectations. Here are some important trends that are going to redefine the way we test software:

  • Artificial Intelligence (AI) and Machine Learning (ML): AI and ML are simplifying testing by dealing with repetitive tasks, determining the extent of test coverage, and predicting potential problems. AI tools can review code, identify patterns, and suggest test cases, so testing is less manual.
  • Shift-Left Testing: Shift-left testing is now becoming a common approach in software testing models. It focuses on finding the bugs at an early stage. This way, problems are found and addressed early.
  • Continuous Testing and Integration (CTI): Software continues to stay stable and bug-free as it evolves by incorporating testing into the continuous integration (CI) pipeline. Issues are identified early and resolved promptly this way.
  • Performance Testing and Monitoring: As the complexity of software and the amount of data it handles increase, it becomes essential to test how well these programs operate. Performance testing and monitoring ensure that the software can process various workloads while remaining responsive.
  • User Experience (UX) Testing : As users expect the software to be easy to use, UX testing is getting even more important. User testing tests how user-friendly and easy-access software is in meeting users’ needs.
  • Security Testing: This type of testing shields software from cyber-attacks and data breaches. It discovers and eliminates weaknesses that can jeopardize the safety of software and user data.
  • Cloud-Based Testing: More individuals are going to test in the cloud because they’re adaptable. This supports continuous testing practices.
  •   Open-Source Testing Tools: They are becoming popular as they are free and customizable testing tools. They allow developers and testers to customize their testing according to specific requirements for individual projects without significant cost.
  •   Automation Testing: Automated testing is becoming more sophisticated, tackling challenging situations without requiring intensive human intervention. This allows testers to concentrate on other issues that are of higher priority.

  Conclusion

In conclusion, the diverse landscape of software testing models within the Software Development Life Cycle (SDLC) offers a range of options to cater to different project requirements and complexities.

From traditional approaches like the waterfall model to more adaptive frameworks like Scrum and Kanban, each model brings its own set of advantages and disadvantages.

The choice of a testing model is crucial, influencing factors such as early issue detection, project adaptability, and overall software quality. As technology evolves, so does the array of testing methodologies, ensuring that software development stays dynamic and responsive to the ever-changing needs of the industry.

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Introduction to Software Testing

  • What is Software Testing: Different Types & Principles
  • Software Testing Tutorial – Know How to Perform Testing
  • Software Testing Life Cycle – Different Stages of Testing
  • How do you make a Career in Software Testing?
  • What is Risk Analysis in Software Testing and how to perform it?
  • Important Software Testing Strategies You Need to Know
  • What are the Different Levels of Software Testing?
  • What is Software Testing Metrics and What are the Types?
  • Software Testing Tools : All You Need To Know About Top Testing Tools
  • What is Decision Table in Software Testing?
  • What is Software Quality Assurance Testing and How does it Work?
  • What is Debugging and Why is it important?
  • What is Agile Testing? Know about Methods, Advantages and Principles
  • What is Domain Testing in Software Testing?
  • What is Acceptance Testing in Software Testing?

Software Testing Types

  • Types of Software Testing : All You Need to Know About Testing Types

What are the Types of Software Testing Models?

  • What are the Differences Functional Testing & Non-Functional Testing?
  • What is Functional Testing? One Stop Solution to Automation Types
  • What is Interface Testing and why do we need it?

Performance Testing

  • Performance Testing Tutorial – What is it & its Types?

Performance Testing Life Cycle : All You Need To Know About Testing Phases

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  • Load Testing using JMeter : How to Measure Performance in CMD
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Mobile Testing (Appium)

  • Introduction to the World of Mobile Application Testing
  • A Complete List of Mobile Application Testing Tools
  • What is Appium & How it Works? | Beginners Guide To Appium
  • Appium Tutorial: Know How to Set up Appium
  • How to Install Appium: Step-by-Step Complete Tutorial
  • A Deconstruction of the Appium Architecture
  • Appium Studio Tutorial: All You Need To Know
  • Java Client For Appium: All you need to know

Automation Testing

  • What is Automation Testing and why is it used?
  • Automation Testing Tutorial: All You Need To Know About Automation Testing
  • Test Automation Strategy: How to Build a good Test automation strategy?

Interview Questions

  • Top 50+ Manual Testing Interview Questions and Answers for Freshers & Experienced
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  • Top 50 Appium Interview Questions You Must Prepare In 2024

task testing models

Today’s world of technology is completely dominated by machines, and their behavior is controlled by the software powering it.  Software testing provides the solution to all our worries about machines behaving the exact way we want them to. This article will provide in-depth knowledge about the different Software Testing Models in the following sequence:

Waterfall Model

Agile model, spiral model, iterative model, software testing models.

Software Testing is an integral part of the software development life cycle . There are different models or approaches you can use in the software development process where each model has its own advantages and disadvantages. So, You must choose a particular model depending on the project deliverables and complexity of the project.

The different Software Testing Models are:

This is the most basic software development life cycle process which is followed broadly in the industry. In this model, the developers follow a sequence of processes downwards towards the ultimate goal. It is like a waterfall where there are various phases involved.

  • Requirement Analysis
  • Analysis phase
  • Software design
  • Programmed implementation
  • Maintenance
  • It is easy to implement and maintain.
  • The initial phase of rigorous scrutiny of requirements and systems helps in saving time later in the developmental phase.
  • The requirement of resources is minimal and testing is done after each phase has been completed.

Disadvantages

  • It is not possible to alter or update requirements.
  • Once you move into the next phase you cannot make changes.
  • You cannot start the next phase until the previous phase is completed.

The V Model is considered superior to the waterfall model. In this model, the development and test execution activities are carried out side by side in the downhill and uphill shape. Also, testing starts at the unit level and spreads towards the integration of the entire system.

  • It is easy to use since testing activities like planning and test designing are done before coding.
  • This model enhances the chances of success and saves time.
  • Defects are mostly found at an early stage and downward flow of defects is generally avoided.
  • It is a rigid model.
  • The software is developed during the implementation phase so early prototypes of the product are not available.
  • If there are changes in the midway, you need to update the test document.

In the Agile model, requirements and solutions evolve through collaboration between various cross-functional teams. It is also known as an iterative and incremental model. The agile software testing model focus on process adaptability and customer satisfaction by rapid delivery of working software product and by breaking the product into small incremental builds.

  • It ensures customer satisfaction with rapid and continuous development of deliverables.
  • The continuous interaction between the customers, developers, and testers makes it a flexible model.
  • You can develop the working software quickly and adapt to changing requirements regularly.
  • It is difficult to assess the effort required at the beginning of the cycle for large and complex software development cases.
  • Due to continuous interaction with the customer, the project can go off track if the customer is not clear about the goals.

This software testing model is similar to the Agile model , but with more emphasis on risk analysis. The different phases of the spiral model include planning, risk analysis, engineering, and evaluation. In this case, you need to gather the requirements and perform the risk assessment at the base level and every upper spiral builds on it.

  • It is suitable for complex and large systems.
  • You can add functionalities depending on the changed circumstances.
  • Software is produced early in the cycle.
  • It is a costly model which requires highly specialized expertise in risk analysis
  • It does not work well on simpler projects.

The Iterative model does not need a full list of requirements before beginning the project. The development process starts with the requirements of the functional part, which can be expanded later. The process is repetitive and allows new versions of the product for every cycle. Every iteration includes the development of a separate component of the system which is added to the functional developed earlier.

  • It is easier to control the risks as high-risk tasks are completed first.
  • The progress is easily measurable.
  • Problems and risks defined within one iteration can be prevented in the next sprints.
  • Iterative model requires more resources than the waterfall model.
  • The process is difficult to manage.
  • The risks may not be completely determined even at the final stage of the project.

These are the different software testing models involved in the software development life cycle. I hope you understood how each of these models is used in software testing.

Now, you can check out the Software Testing Fundamentals Course  by  Edureka.  This course is designed to introduce you to the complete software testing life-cycle. You will be learning different levels of testing, test environment setup, test case design technique, test data creation, test execution, bug reporting, CI/CD pipeline in DevOps, and other essential concepts of software testing.

Got a question for us? Please mention it in the comments section of “Software Testing Models” and we will get back to you.

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What Is Model-Based Testing (MBT)?

Model-Based Testing (MBT) is an advanced software testing approach that uses abstract models to automate the generation of test cases. It’s a great way to ensure software quality. With MBT, various conditions and inputs are simulated to ensure that software meets specifications and responds reliably under unforeseen scenarios.

You can reduce the time spent on test creation and execution by 20% to 50% compared to traditional methods. Plus, MBT systematically enhances quality assurance by ensuring that every test scenario is covered. Model-based testing enables teams to keep up with rapid development cycles while maintaining the highest quality standards.

  • MBT Process
  • Paradigm Shift
  • Your Gateway to MBT

The MBT Process Explained

It all starts with a precise model that encapsulates the software’s intended functionality and possible states. Usually, a model is graphically represented using one or more UML diagrams. This model is not just a sketch but a comprehensive map that guides every test scenario.

With this blueprint in place, MBT automates the creation of test cases that mirror every possible state transition and user interaction within the software. These test cases are used to explore the system’s responses and ensure that every path leads to the expected outcome.

Create Models

Develop specific models from requirements that represent functional sequences, user interfaces, and data flows.

Generate Test Cases

Automatically generate test cases from models that represent different scenarios for using the software.

Execute Tests

Test the software against the generated test cases, manually or automatically, to compare the actual behavior with the modeled behavior.

Analyze Results

Evaluate whether the software performs as expected; identify defects or areas for improvement.

Iterate and Refine

Continuously adapt and improve models and test cases based on test results.

The Paradigm Shift in Testing

As software becomes more complex, traditional linear and manual testing methods struggle to keep up. They can be time-consuming, error-prone, and simply can’t keep pace with rapid development cycles. With MBT, you can say goodbye to the daunting task of manually crafting separate test cases for each state of the software under test. MBT uses models as master keys to unlock multiple test scenarios, making thorough coverage a breeze.

MBT surpasses traditional approaches by utilizing abstract representations to automatically generate and execute test cases. This enhances test coverage while also increasing efficiency and accuracy. Plus, with MBT, you can rest assured that your testing is not only effective but also stakeholder friendly and approachable. Testing with MBT shifts the focus from reactive to proactive, anticipating potential issues before they arise.

Your Gateway to Model-Based Testing

MBTsuite is your one-stop solution for model-based testing. The tool simplifies the model-based testing process by offering a user-friendly MBT workspace. It covers the whole MBT process in one convenient web application:

MBTsuite is designed to be adaptable to any fast-paced development environment. Try it now to take your software testing to the next level.

Model view in MBTsuite

Model-Based Testing FAQ

What is model-based testing (MBT)?

Model-Based Testing (MBT) is an advanced software testing approach that uses abstract models to automate the generation of test cases. This method models software behavior, including states and transitions, to verify that the software functions correctly and meets its specifications. MBT is an effective method for identifying defects early in the development cycle, ensuring thorough test coverage, and maintaining quality across rapid development iterations. It abstracts the complexity of the system, providing a clear and efficient path to robust software quality assurance.

What are the core components of model-based testing?

The core components of Model-Based Testing (MBT) include

  • Abstract Model Building: Creating a graphical representation of the software’s functionality and possible states.
  • Test Case Generation: Using the model to automatically generate test cases that cover all possible scenarios and paths.
  • Test Execution: Applying the generated test cases to validate the software against the expected behavior outlined in the model.
  • Results Analysis: Evaluate the results of the tests to identify deviations and areas for improvement.
  • Model Refinement: Updating the model based on test results and evolving requirements to improve accuracy and coverage.

In what ways does MBT improve the efficiency of the testing process?

Model-Based Testing (MBT) improves efficiency by automating test case creation from a model, reducing time and effort compared to manual test design. MBT abstracts software behavior, enabling simultaneous generation of multiple scenarios for faster comprehensive coverage. This automation reduces repetitive work and errors. Additionally, MBT quickly adapts to changing requirements by automatically updating test cases to reflect new conditions. This maintains testing momentum and minimizes the need for time-intensive manual revisions, keeping the focus on delivering quality software swiftly.

What is MBTsuite, and how does it facilitate MBT?

MBTsuite is a web-based tool that simplifies the creation, management, and execution of test models for Model-Based Testing (MBT). It transforms software requirements into dynamic graphical models and generates detailed test cases for use in test automation tools, improving efficiency and accuracy. MBTsuite’s user-friendly interface and collaborative features enable teams to develop and share models in real-time, promoting a unified approach to quality assurance. Its cloud-based platform provides flexibility and accessibility, making advanced MBT practices accessible to all software development stakeholders.

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  • Software development

Agile methodology testing best practices & why they matter

There's still a need for manual testing–but not in the way you might think!

Dan Radigan

Browse topics

Waterfall project management separates development and testing into two different steps: developers build a feature and then "throw it over the wall" to the quality assurance team (QA) for testing. The QA team writes and executes detailed test plans. They also file defects when painstakingly checking for regressions in existing features that may have been caused by new work.

Many teams using these waterfall or other traditional testing models find that as the product grows, the amount of testing grows exponentially–and QA invariably struggles to keep up. Project owners face an unwelcome choice: delay the release, or skimp on testing. (I'll give you one guess as to which option wins 99% of the time.) In the mean time, development has moved onto something else. So not only is  technical debt  mounting, but addressing each defect requires an expensive context switch between two parts of the code base. Insult, meet injury.

To make matters worse, QA teams are traditionally rewarded according to how many bugs they find, which puts developers on the defensive. What if there was a better way for both developers and QA to reduce the number of bugs in the code while also eliminating those painful trade-offs project owners have to make? Wouldn't it create better all-around software?

Enter agile and DevOps testing .

Moving from traditional to agile testing methods

The goal of agile and DevOps teams is to sustainably deliver new features with quality. However, traditional testing methodologies simply don't fit into an agile or DevOps framework. The pace of development requires a new approach to ensuring quality in each build. At Atlassian, the way we test is agile. Take a detailed look at our testing approach with Penny Wyatt, Jira Software's Senior QA Team Lead.

Let's be clear: scripted manual testing is technical debt.

Much like compounding credit card debt, it starts with a small amount of pain, but snowballs quickly–and saps the team of critical agility. To combat snowballing technical debt, at Atlassian we empower (nay: expect) our developers to be great champions for quality. We believe that developers bring key skills that help drive quality into the product:

  • Developers are great at solving problems with code.
  • Developers that write their own tests are more vested in fixing them when they fail.
  • Developers who understand the feature requirements and their testing implications generally write better code.

We believe each user story in the backlog requires both feature code and automated test code. Although some teams assign the developers the feature code while the test team takes on automated testing, we find it's more effective to have a single engineer deliver the complete set.

Treat bugs in new features and regressions in existing features differently. If a bug surfaces during development, take the time to understand the mistake, fix it, and move on. If a regression appears (i.e., something worked before but doesn't anymore), then it's likely to reappear. Create an automated test to protect against that regression in the future.

This model doesn't mean developers work alone. It's important to have QA engineers on the team as well. QA brings an important perspective to the development of a feature, and good QA engineers know where bugs usually hide and can advise developers on probable "gotchas."

Human touch through exploratory testing

On our development teams, QA team members pair with developers in exploratory testing, a valuable practice during development for fending off more serious bugs. Much like code review, we’ve seen testing knowledge transfer across the development team because of this. When developers become better testers, better code is delivered the first time.

Exploratory testing makes the code, and the team, stronger.

But isn't exploratory testing manual testing? Nope. At least not in the same sense as manual regression testing. Exploratory testing is a risk-based, critical thinking approach to testing that enables the person testing to use their knowledge of risks, implementation details, and the customers' needs. Knowing these things earlier in the testing process allows the developer or QA engineer to find issues rapidly and comprehensively, without the need for scripted test cases, detailed test plans, or requirements. We find it's much more effective than traditional manual testing, because we can take insights from exploratory testing sessions back to the original code and automated tests. Exploratory testing also teaches us about the experience of using the feature in a way that scripted testing doesn't.

Maintaining quality involves a blend of exploratory and automated testing. As new features are developed, exploratory testing ensures that new code meets the quality standard in a broader sense than automated tests alone. This includes ease of use, pleasing visual design, and overall usefulness of the feature in addition to the robust protections against regressions that automated testing provides. 

Change can be hard–really hard

I'll leave you with a personal anecdote that nicely summarizes my journey with agile testing. I remember managing an engineering team early in my career that had strong resistance to writing automated tests, because "that work was for QA". After several iterations of buggy code and hearing all the reasons why automated testing would slow the team, I put my foot down: all new code had to be proven by automated tests.

After a single iteration, code started to improve. And the developer who was most adamantly against writing tests turned out to be the one who sprung into action when a test failed! Over the next few iterations the automated tests grew, scaled across browsers, and made our development culture better. Sure, getting a feature out the door took longer. But bugs and rework dropped significantly, saving us huge amounts of time in the end.

Change is rarely easy. But like most things worthwhile, if you can buckle down and create new patterns for yourself, the only question you'll be left with is "Why didn't we do this sooner?!"

Agile has had a huge impact on me both professionally and personally as I've learned the best experiences are agile, both in code and in life. You'll often find me at the intersection of technology, photography, and motorcycling. 

Incident response for agile development

When bugs in production, incidents, and downtime happen, learn how to build user trust by applying agile values to your incident response.

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Software Testing Methodologies and Models

Home / IT Strategy / Software Testing Methodologies and Models

Perfect software does not exist, and every program has potential failure points. Software testing is a software development lifecycle stage during which the team discovers unwanted errors in a program or system.

Different testing methodologies help pinpoint several types of software errors. Knowing how each software testing model works is essential to building, deploying, and maintaining a high-quality testing strategy and software.

software testing methodologies and models.

Why Is Testing Important in SDLC?

The testing phase is a critical stage in the software development lifecycle . It comes after software implementation, and testing aims to discover and fix software errors.

SDLC testing phase.

Software testing is crucial because the product goes into production after testing. Every software development team must deliver quality software for two reasons:

  • The end user benefits from having a high-quality product built according to requests and specifications.
  • The development team's integrity and the possibility of new projects depend on the software’s quality.

A software testing team performs various checks for different issues separately from developers. This approach aids in having a team solely focused on discovering problems and allows for implementing continual development.

Software Testing Methodologies

Software testing is a formal process performed by a testing team to help confirm a program is logically correct and valuable. Testing requires using specific test procedures and creating test cases.

Software testing is performed in two stages:

  • Verification - Is the system built correctly?
  • Validation - Is the system constructed according to the user requirements?

Software testing uses several methodologies and models to answer these two questions.

Black Box Testing

In the black box testing methodology, a program is a closed (black) box with unknown details. The only visible components to a tester are the program inputs and outputs.

A tester can determine whether a program is functional by observing the resulting outputs for various inputs. Black box testing does not consider a program’s specifications or code, only the expected behavior for different test cases. Black box testers do not necessarily have to be very skilled since they do not interact with any code.

Black box testing.

Black box testing comes with both benefits and drawbacks. The critical advantage of black box testing is there is no requirement to work with code and programming logic. However,   testing for all input combinations is impossible.

Three types of tests are based on the black box testing methodology: functional testing, non-functional testing, and regression testing .

Functional Testing

Functional testing checks whether the software performs a specific function without considering which component within the system is responsible for the operation.

The testing team checks functionalities for both good and bad inputs. An example function is a user login page behavior. A functional test checks whether a user can log in with the correct credentials or not log in with incorrect credentials.

As the software’s complexity increases, so does the number of functions within the software. The order of testing functions is crucial for an efficient and functional testing strategy. As functionalities are often nested, the behavior of the software depends on the order of steps a person takes when using the software.

The main benefit of functional testing is that the testing team can check individual functionalities before all software components are completed. The probability of detecting errors in functional testing is exceptionally high, as it shows problems when using software from a user's perspective.

Non-Functional Testing

The non-functional testing method verifies software aspects apart from functionalities and features. This testing method focuses on how a program performs certain actions under specific conditions.

Non-functional testing helps uncover if a program is usable from a user's perspective. The method checks for usability issues, such as cross-compatibility across different devices, browsers, or operating systems.

Regression Testing

Regression testing is a software testing method that ensures all new code changes do not cause issues in previously tested components and negatively affect system stability. The testing method repeats tests from previous iterations, ensuring that the latest changes do not cause unwanted effects on existing code.

Regression testing is necessary after any program action that results in changes to the original code, such as:

  • Implementing new requirements.
  • Adding new functionalities to the program.
  • Removing existing functionalities.
  • Fixing any defects.
  • Optimizing for better performance.

If software changes often, the best approach is to use automated testing tools and create reusable tests for multiple iterations and cycles of regression testing.

White Box Testing

The white box testing method is the opposite of black box testing. In this method, the program is an open (white) box whose inner workings are known to the tester.

White box testing.

White box testing analyzes the source code and requires the following skillset:

  • Coding and scripting knowledge.
  • Familiarity with the specific programming language in use.
  • The design of particular components.

Testers form a plan based on the program's structure. For example, white box testing can include creating scripted tests that go through the whole code and run every function. Specific tests can check whether there are infinite loops or cases where the code does not run.

The main drawback of white box testing is the number of test iterations, which increases as the application becomes more complex. The method requires creating a strategy where recursions or loops execute fewer times for carefully chosen and representative values.

Three types of tests are based on the white box testing methodology: statement testing , path testing , and branch testing .

Statement Testing

Statement testing is a testing technique within white box testing. The method assesses all executable statements in the code at least once. For example, if a code block contains several conditional statements, the statement testing technique involves going through all input iterations to ensure all parts of the code execute.

The statement testing technique discovers unused parts of code, missing referenced statements, and leftover code from previous revisions. As a result, statement testing helps clean up the existing code and reduces redundant code or adds missing components.

Path Testing

Path testing creates independent linear paths throughout the code. The testing team creates a control flow diagram of the code, which aids in designing unit tests to evaluate all code paths.

Analyzing different paths helps discover an application’s inefficient, broken, or redundant flows.

Branch Testing

Branch testing maps conditional statements in the code and identifies the branches for unit testing. The branch types are:

  • Conditional : Executes when a condition is fulfilled.
  • Unconditional : Executes regardless of any circumstances.

For example, the following code contains several nested statements:

A tester identifies all conditional branches. In the example code, conditional branches are W , X , and Z because the statements only run under a specific condition. On the other hand, Y is an unconditional branch because it always executes after the X statement.

Branch testing aims to execute as many branches as possible and test for all branching conditions.

Note: Check out our article on Black Box Testing vs White Box Testing to learn more about the differences between these two testing methodologies.

Functional testing is a subtype of black box testing that considers the software specifications for a given function or program. The testing method provides various inputs and analyzes the outputs without considering the internal software structure.

Functional testing involves 4 distinct steps that start from more minor parts of the code and branch out into evaluating the entire system. The model aims to analyze a component or the program's compliance and check whether a system does what it is supposed to do according to specifications.

Step 1: Unit Testing

Unit testing is a software testing methodology that validates individual parts of source code. A unit test helps determine the functionality of a particular module (unit), and the process isolates individual parts to decide whether they function correctly.

A unit is an individual function, procedure, or object-oriented programming class. Typically, unit testing helps validate the front-end interface.

The main benefit of unit testing is early problem discovery in the development lifecycle. In test-driven development, such as scrum or extreme programming, testers create unit tests before any code exists.

The main drawback when applying unit testing is the need to evaluate complex execution paths in a program. Unit tests are localized and incompatible for discovering integration or system-wide errors.

Step 2: Integration Testing

Integration testing is a phase that comes after unit testing. The method combines previously assessed individual program units (modules) into larger groups and performs tests on the aggregates.

There are several different approaches to integration testing:

  • The bottom-up strategy first evaluates and integrates low-level components before moving to more complex groups.
  • The top-down approach uses reverse engineering to assess components from more complex groups and simplifies them into smaller units.
  • Sandwich (hybrid) testing combines the bottom-up and top-down strategy by simultaneously testing low and high-level components.
  • Big Bang testing combines all components into a single large unit for testing. The method does not have an incremental approach compared to other testing methods.

Integration testing validates the connections between the front-end interface and an application's back end.

Step 3: System Testing

System testing performs tests on a completely integrated system. The step analyzes the behavior and compares them to expected requirements ( Quality Assurance ), validating a fully integrated software.

System testing aims to discover issues missed by integration and unit testing and to provide a comprehensive overview of whether the software is ready for release. The different testing approaches in system testing consider how well the software works in various environments and how usable the software is.

The main challenge of system testing is designing a strategy that fits within the available time and resource constraints while providing a comprehensive analysis of the entire system after integration.

Note: To easily scale out for testing purposes, we recommend using Kubernetes on BMC . It provides on-demand production-ready cloud-native environments.

Step 4: Acceptance Testing

The final part of functional testing is the acceptance test. The testing method aims to assess the approval of the application's end-user. The approach engages end-users in the testing process and gathers user feedback for any potential usability issues or missed errors during any previous testing phases.

Acceptance testing falls into one of the two following categories:

  • User acceptance testing allows target users to evaluate and use the software through beta testing or a similar strategy. The user base determines whether the software operates as expected.
  • Operational acceptance testing checks whether the software is functional and operates as expected. The test examines various software components, such as security, backup and disaster recovery, and failover testing.

After acceptance testing, the software is ready for production if the results meet the acceptance criteria. Otherwise, the software gets pushed back into previous development and testing phases if the testing does not pass the threshold.

Non-functional testing evaluates the software from the users’ perspective, focusing on the user experience. The testing methodology aims to catch issues unrelated to a software's functionality but essential to the user's experience.

Non-functional testing considers parameters such as:

  • Reliability
  • Scalability
  • Availability

The focus of non-functional testing is on how a product operates rather than how it behaves in specific use cases. This testing model is conducted through performance testing , security testing , usability testing and compatibility testing .

Performance Testing

Performance testing checks the speed, scalability, and stability of the software. Several different performance testing subtypes exist, such as:

  • Load tests check how the software functions under regular user demand.
  • Stress tests examine how software behaves under a high user load or other complex circumstances.
  • Spike tests check how software behaves under sudden high user load spikes.
  • Endurance tests show an application's stability over an extended time.

Performance testing.

All performance tests aim to catch and fix low latency and performance problems that degrade a user's experience.

Security Testing

Security testing checks for any security issues in software and is one of the most critical software testing methodologies. The method checks for any vulnerabilities within the system and possibilities of cyber attacks .

Methods such as penetration testing and vulnerability scanning help discover and lower security risks within the software, and there are also numerous penetration testing tools to automate the testing process.

Usability Testing

Usability testing evaluates how user-friendly and convenient software is to a user. The tests highlight how quickly an unguided user can do something in the program or application . The usability test results show how quickly a new user can learn to use the software and whether any interface improvements are necessary.

Compatibility Testing

Compatibility testing shows a system's behavior in various environments and with other software. The method focuses on integration with existing solutions and technologies.

Software Testing Models

The testing phase in the software development lifecycle is not the only place where errors can be identified and fixed. All development stages benefit from including software tests.

Continuous software development also requires continuous software testing. Software development should work with the testing team to discover potential problems early on or to determine places where testing is impossible. Early discovery is better, and as the steps progress, the cost of finding and fixing errors increases. According to the IBM System Science Institute, the relative cost of discovering and repairing defects in the maintenance phase is around six times higher .

Therefore, it is crucial to see how testing integrates into various software development processes and methodologies. Below is an overview of well-known software development models and how testing integrates into each method.

Note: Learn more about about continous software development, integration and testing in our article on CI/CD .

Waterfall Model

The waterfall model is a software development method divided into sequential steps or stages. The team progresses to the following stage only after finishing the previous phase.

The testing team starts creating a test plan and strategy during the requirements phase in the waterfall model. Once the software goes through the implementation phase, testers verify if the software works correctly and according to specifications.

Waterfall model testing phase.

The main benefit of the waterfall method in software testing is that the requirements are well-defined and easily applied in the testing phase. The model is unfit for projects where conditions change often and unplanned events occur.

  • Simple . A high level of abstraction simplifies the communication process with the end user, who does not need to know the technical process details to participate in the development process.
  • Easy to follow . Project managers have access to critical points in project development, which allows them to be aware of the level of progress during development.
  • Easy to apply . The model goes through a single iteration, which is convenient for replacing existing solutions with new software.

Disadvantages

  • No feedback loop. The waterfall model lacks a feedback mechanism and multiple iterations. Defining all requirements at the start of the project is impossible and returning to any previous step to make changes is not a supported path in the model.
  • No apparent connection between phases . The model assumes that every previous development phase is the input for the following step. However, the model does not define how requirements transform into a design.
  • Not focused on problem-solving . The waterfall model approaches software design as a hardware or production mechanism. On the other hand, software design requires testing and trying different approaches. The model limits any modular and creative process during software development.
  • Limited end-user interaction . The waterfall model communicates with a user only in the first step of the development phase and in the final stage when the product is complete. The approach limits user interaction, which makes the development process inefficient.

The V model is an extension and improvement of the waterfall model. The model is divided into sequential steps, with additional testing steps for each development phase. The V model goes through all the stages in functional testing to verify and validate the software.

V model testing phases.

The shape of the V model shows the corresponding test phases to the development life cycle phases. When viewed left to right, the model demonstrates the order of steps as time progresses, while viewing from top to bottom reveals the abstraction level.

  • Feedback mechanism . The V model is practical for simple and complex projects due to the possibility of returning to any of the previous phases.
  • Verifies and validates . The model checks whether a project is developed well, fully implemented, and fulfills user requirements.
  • High-quality products . The development process is well-organized and controlled, guaranteeing the software's quality.
  • Testing in early phases . The testing team participates in the early development phases, which results in a better understanding of the project from the start. The model significantly saves time and resources on testing in the later stages of development.
  • Not flexible . When problems appear, the team must update all phases of the software development process, including documentation. Any change slows down the SDLC.
  • Costly . Implementing the V model requires significant resources to support numerous development teams. The model is better suited for larger projects and businesses.

Agile Model

The agile methodology is a fast and iterative approach to developing software that follows the principles defined in the Agile Manifesto . It breaks down software development into small increments and multiple iterations. The agile model allows constant interaction with end users, and requirements change constantly.

Agile testing phase.

Testing in the agile model happens in every iteration. Software testing in this environment requires continual testing throughout the CI/CD pipeline via automated testing tools and frameworks .

  • Fast development . Deploying software happens quicker than in other models. The model is adaptive and responsive to changes, resulting in shorter turnaround times.
  • Smaller iterations . Errors and defects are easier to spot and analyze in smaller chunks. Delivering software takes less time, and new iterations happen often.
  • High level of user interaction . Constant end-user feedback ensures acceptance testing happens often. As a result, the product is closer to the requirements with each iteration.
  • Unpredictable . Although the testing team gathers user feedback, there is no guarantee the next iteration will contain these changes. The fast pace creates an unpredictable product roadmap.
  • Costly . Continual releases to production and development result in higher expenses. Predicting the necessary effort for each change becomes difficult.
  • Phase overlap . Every iteration goes through all the development phases. As sprints progress, it becomes trickier to distinguish who is responsible for which task.

Scrum Model

The scrum model is a project management approach that uses principles from the agile model. The model is goal-oriented and time-constrained into iterations known as sprints. Every sprint consists of meetings, milestones, and events managed by a scrum master.

The scrum model does not feature a testing team, and developers are responsible for constructing and implementing unit tests. The software is also often tested by the end user in each sprint.

Some scrum teams do feature testers, in which case testers must provide time estimations for every testing session during scrum meetings.

  • Fast-paced . The scrum model ensures project delivery happens quickly, which makes it suitable for fast-paced projects under development.
  • Cost efficient . Every sprint consists of several members, and the fast-paced environment ensures the project completion happens within a specified time frame.
  • High-level of user interaction . Like the Agile model, scrum releases code often, which results in constant user interaction. Continual feedback results in satisfying user requirements through every sprint.
  • High chance of failure . The scrum model requires a high level of interaction and commitment. Daily meetings are stressful, and one team member leaving impacts the whole project. The software is at a higher risk of failure in a non-compliant team.
  • Lacks quality . When a model lacks a testing team, the quality of the software is unacceptable. The resulting software is of lower grade than those undergoing intensive testing.

DevOps Model

The DevOps model combines continuous testing into every development stage, while also having a dedicated testing role in the team. The goal of testing in the DevOps pipeline focuses on software quality and risk assessment.

Automated testing and test-driven development improve code reliability, which helps minimize the likelihood of new builds breaking existing code.

  • Fast software delivery . DevOps improves the delivery speed for new features and bug fixes. Quick response times to issues improve customer satisfaction with the product.
  • Quality software . Automated testing and continuous deployment improve software quality. DevOps ensures every change is tested before deploying software to production.
  • Error efficient . Integrating testing in the initial stages of development avoids having to fix problems later in the development cycle.
  • Difficult to implement . DevOps requires integrating development with operations and following DevOps principles , which is hard to achieve in large organizations with complex systems.
  • Increased risks . DevOps heavily relies on automation, which leads to problems if not configured properly. Issues are difficult to track down when they do occur.
  • Excessive costs . The model requires significant investments into infrastructure and DevOps tools . A misconfigured system creates integration challenges that are difficult to manage.

Learn how to set up a test sandbox environment you can easily scale for production workloads.

Iterative Development

Iterative development divides software development steps into subsystems based on functions. The method is incremental, and each increment delivers a completed system. Every new iteration improves existing processes within every subsystem.

Iterative development testing phases.

Early releases provide a primitive version of the software, while every following release improves the quality of the existing functionalities. Testing is simpler in early phases and increases in complexity as iterations progress.

  • Risk control . Iterative development allows starting with high-risk tasks first. The controlled nature of the development method enables improving any issues through iterations.
  • Easy to follow . Every iteration shows an improvement from the previous. Measuring changes between iterations becomes simple for project managers.
  • Early training . Since iterative development delivers a simpler version of the full software, users are engaged and use the software early. User feedback is based on the whole system, and improvements are simpler to implement in the following iterations.
  • Specialized versions . Versioning software becomes simple, as development teams can focus on specific improvements with each iteration. For example, one iteration improves the user interface, while the following iteration improves the software's performance. The approach simplifies the test design process.
  • Costly . Every iteration requires the presence of the whole software development team.
  • Lacks full specifications . The iterative development approach starts creating a system from simple requirements. As time progresses, the conditions also change. The fluidity of specifications makes it challenging to see when the project ends.
  • Hard to manage . Iterative development requires intense project management and risk assessment for each iteration. As projects grow, the system's complexity increases.

Spiral Model

The spiral model is an agile model with a focus on risk assessment. The model combines the best qualities of the top-down and bottom-up development methods. The method uses the waterfall model phases as increasingly complex prototypes.

As risk analysis is the focus of every step, the spiral model enables the early discovery of faults and vulnerabilities. The model performs an early assessment of issues, which makes security testing less costly in the long run.

  • Flexible . The spiral model is adaptable to requirement changes, even after developing a feature. There is less strain on the testing team when reporting issues.
  • Secure . Risk analysis is in every step of the development process, and the iterative approach also helps manage risk. The spiral model focuses more on software security compared to other development models.
  • High level of user interaction . The iterative approach in the spiral model allows engaging the customer in the development process and receiving continual feedback. The development team quickly makes changes during development, which saves costs and resources in the long run.
  • Costly . The spiral model best suits large projects and complex software.
  • Specialized risk analysts . The model depends on having high-quality risk analysts included in every development step for efficient risk assessment.
  • Complex . The model is hard to follow and requires an increased focus on protocols and documentation in every step of development.
  • Time management . The duration of each phase is unknown. The model is susceptible to exceeding the budget and falling behind on time constraints.

Note: Learn more about the Automated Security Testing Best Practices .

The RAD (Rapid Application Development) model is an agile methodology that combines prototyping with iterative development. The method focuses on gathering user requirements, while the rest of the development process has no specific plan or steps.

RAD is a fast-paced technique that focuses on creating reusable components that serve as templates for following projects or prototypes. The testing team assesses all prototypes in every iteration and immediately integrates the components into the final product.

  • Flexible . The model quickly implements requirements changes and accommodates new user requirements.
  • Reusable . Creating reusable prototypes provides a templating approach to development, increasing code reusability and requiring less effort in the long run.
  • Fast . The development time and short iterations enable continual software integration and rapid delivery. The RAD model incorporates various automation tools to speed up the development process.
  • High expertise dependency . RAD-based teams are small, highly skilled, and technically strong. The RAD model requires an experienced team of developers to identify and model user requirements.
  • Only suitable for modular systems . Not all software has clear-cut components. RAD requires creating smaller prototypes that are reusable. Complex systems require specialized features which do not apply to all use cases.

Extreme Programming

Extreme programming (XP) is an agile method for developing software best suited for small to medium-sized teams (6-20 members). The technique focuses on test-driven development and short iterations that provide users with direct results.

XP has no strict methodology for the development team to follow. Instead, the method provides a flexible framework where procedures or the sequence of steps changes depending on the activity. The Agile Manifesto principles, and techniques like  pair programming , are vital components in XP.

  • Focus on software development . The team creates software rather than focusing on meetings and documentation. The work environment is comfortable for developers, with many opportunities to learn and improve skills.
  • Short development time . The lack of rules and procedures makes the software delivery speedy. Quick results are beneficial to customers.
  • Test-driven development . Unit tests exist before writing code, and programmers know what is tested before creating software. This approach stimulates programmers to take better precautions while coding.
  • Hard to implement . The method is tough to implement. XP requires programmers with strict discipline who accept and follow the required principles and can work together closely.
  • Client dependent . The client chooses whether to participate in the development process. A non-participating client often leads to unsatisfactory software.

High-quality software testing is what differentiates between quality software and a lackluster project. The importance of software testing is crucial to development, which is why there are so many testing methodologies and approaches. Development teams should follow trends in software testing and be ready to fundamentally change their approach to profit from new software testing methodologies and models.

Next, check out how automated testing frameworks help streamline the testing process and improve testing speeds during the testing phase.

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What is Software Testing? The 10 Most Common Types of Tests Developers Use in Projects

Nahla Davies

Software development and testing go hand in hand. And in the era of agile software development, with quick releases of small iterations, you should do testing more and more frequently.

In order to perform effective testing, you need to know about the different types of testing and when you should use them.

In this article, I'll discuss some of the tests available to you to help you ensure the operability, integrity, and security of your products and apps.

The Software Testing Pyramid

The Software Testing Pyramid

The software testing pyramid covers all stages of the software development life cycle (SDLC). It extends from unit testing at the base, through to integration testing, and concludes with functional testing at the apex.

There is no set distribution among these types of testing. Instead, you should determine which tests best suit your individual needs. In order to make these decisions about the types of testing you need, you should balance their cost, how long they'll take, and how many resources they'll require.

Agile software developers also use software testing quadrants that categorize tests based on whether they are business-facing or technology-facing, and whether they critique the product or support the team.

Unit testing, for example, is a technology-facing test that supports the team, whereas usability testing is a business-facing test that critiques the product.

Let's go over some important types of testing now.

Unit Testing Definition

Unit testing involves testing individual code components rather than the code as a whole. It verifies the operation of all your component logic to identify bugs early in the SDLC, which allows you to correct errors before further development.

Unit testing is known as “white box” testing, because testing occurs with full knowledge of the application's structure and environment.

One example of unit testing is to create mock objects for testing sections of code, such as functions with variables that have not yet been made.

Integration Testing Definition

A step up from unit testing is integration testing, which combines individual components and tests them as groups. Integration testing identifies issues in how the individual components interact with each other to see if the code meets all its functional specifications.

Integration testing differs from unit testing in that it focuses on modules and components working independently in relation to the overall group. On the other hand, unit testing focuses on isolating the modules or components before testing.

The point of integration testing is to expose any issues or vulnerabilities in the software between integrated modules or components.

As a more simplified example, if you were to perform an integration test of an email service you’re building, you would need to test the individual components such as Composing Mail, Saving Drafts, Sending, Moving to Inbox, Logging Out, and so on.

You would perform a unit test of the individual features first, followed with the integration test for each of the functions that are related.

End-to-end Testing Definition

At the top of the pyramid is end-to-end (E2E) testing. As its name suggests, end-to-end testing replicates the full operation of the application in order to test all of the application’s connections and dependencies. This includes network connectivity, database access, and external dependencies.

You conduct E2E testing in an environment that simulates the actual user environment.

You can determine the success of an E2E test using several metrics, including a Status of Test (to be tracked with a visual, such as a graph), and a Status and Report (which must display the execution status and any vulnerabilities or defects discovered).

Types of Software Testing

Within the levels of the testing pyramid are a wide variety of specific processes for testing various application functions and features, as well as application integrity and security.

Application Security Testing Definition

One of the most important types of testing for applications is application security testing. Security testing helps you identify application vulnerabilities that could be exploited by hackers and correct them before you release your product or app.

There are a range of application security tests available to you with different tests that are applicable at different parts of the software development life cycle.

You can find different types of application security testing at different levels of the testing pyramid. Each test has its own strengths and weaknesses. You should use the different types of testing together to ensure their overall integrity.

Static Application Security Testing (SAST) Definition

You should use static application security testing (SAST) early in the SDLC. This is an example of unit testing.

SAST reflects the developer’s knowledge, including the general design and implementation of the application, and it is therefore white box, or inside out, testing.

SAST analyzes the code itself rather than the final application, and you can run it without actually executing the code.

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According to the security analysts at Cloud Defense ,

“SAST checks your code for violation of security rules and compares the found vulnerabilities between the source and target branches...you'll then get notified if your project’s dependencies are affected by newly disclosed vulnerabilities.”

Once you're aware of vulnerabilities, you can resolve them before the final application build.

You should apply SAST in the development phase of your software projects. A good approach for you will be to design and write your applications to include SAST scans into your development workflow.

Dynamic Application Security Testing (DAST) Definition

On the other end of the spectrum is dynamic application security testing (DAST), which tests the fully compiled application. You design and run these tests without any knowledge of the underlying structures or code.

Because DAST applies the hacker’s perspective, it is known as black box, or outside in, testing.

DAST operates by attacking the running code and seeking to exploit potential vulnerabilities. DAST may employ such common attack techniques as cross-site scripting and SQL injection.

DAST is used late in the SDLC and is an example of integration security testing. While slow (a complete DAST test of a complete application can take five to seven days on average), it will reveal to you the most likely vulnerabilities in your applications that hackers would exploit.

Interactive Application Security Testing Definition

Interactive application security testing (IAST) is a newer testing methodology that combines the effectiveness of SAST and DAST while overcoming the issues associated with these more established tests.

IAST conducts continuous real-time scanning of an application for errors and vulnerabilities using an inserted monitoring agent. Even though IAST operates in a running application, it is considered an early SDLC test process.

Regardless of what type of software you’re looking to test, IAST is best used in a QA (Quality Assurance) environment, or an environment that is designed to replicate production as closely as possible without your clients or customers actually accessing it.

Compatibility Testing Definition

Compatibility testing assesses how your application operates and how secure it is on various devices and environments, including mobile devices and on different operating systems.

Compatibility testing can also assess whether a current version of software is compatible with other software versions. Version testing can be backward or forward facing.

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Examples of compatibility testing include:

  • browser testing (checking to make sure your website or mobile site is fully compatible with different browsers)
  • mobile testing (making sure your application is compatible with iOS and Android)
  • or software testing (if you’re going to be creating multiple software applications that need to be interacting with one another, you’ll need to conduct compatibility testing to ensure that they actually do so).

Beyond the Software Testing Pyramid

Modified versions of the testing pyramid can include a level that's next to or above end-to-end testing. This level consists of tests focused on the application user.

Performance Testing Definition

You need to know how the application will work in a variety of different conditions, and this is the purpose of performance testing. Performance testing can model various loads and stresses to assess the robustness of the application. The type of performance testing is based on the applied conditions.

An example of performance sting is load testing, which determines the maximum load applied to the system at the time of a crash.

Another example like scalability testing, on the other hand, applies a gradually increasing load to the system to assess ways to accommodate the added system stresses.

And spike testing assesses the effect of applying sudden large load changes to the system.

You should conduct performance testing on any software system before you put it to market. Test it against stability, scalability, and speed so you can identify what to fix before going live.

Usability Testing Definition

Testing the actual use of the application interface is an important task. It is one thing to understand if the application functions as designed. It is another thing to understand if the design itself is acceptable to users. This is where usability testing comes in.

With usability testing, developers can assess user reactions to specific application features and functions. This includes features that you may know in advance will be less desirable from the user perspective but are necessary for strong security and proper operation (like strong password requirements).

Usability testing is not so much about cosmetic issues or fixing grammar errors in any written text (although both of those issues are certainly important in their own right). Instead it's about how easy the completed application is to use by the end user.

Testing is not just something the QA division should do after you have finished developing an application. It's also important part of the software development process .

Knowing what tests are available to you and how they work will help you ensure your application functions well, is secure, and is acceptable to the end user.

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Beyond Accuracy: Behavioral Testing of NLP Models with C heck L ist

Marco Tulio Ribeiro , Tongshuang Wu , Carlos Guestrin , Sameer Singh

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[Beyond Accuracy: Behavioral Testing of NLP Models with CheckList](https://aclanthology.org/2020.acl-main.442) (Ribeiro et al., ACL 2020)

  • Beyond Accuracy: Behavioral Testing of NLP Models with CheckList (Ribeiro et al., ACL 2020)
  • Marco Tulio Ribeiro, Tongshuang Wu, Carlos Guestrin, and Sameer Singh. 2020. Beyond Accuracy: Behavioral Testing of NLP Models with CheckList . In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics , pages 4902–4912, Online. Association for Computational Linguistics.
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Task-based Usability Testing + Example Task Scenario

Task-based Usability Testing + Example Task Scenario

TABLE OF CONTENTS

User experience is an incredibly important aspect of digital products nowadays. When failing to continuously test and optimize the usability of your website it may seem chaotic or out of date. With  usability testing  studies you can get valuable insights and user feedback, and make design decisions based on reliable data , rather than guesswork.

Today we will be focusing on task-driven usability tests. We will talk about what benefits usability testing and how you can implement it remotely within minutes with our ready-to-go example and a 10-step process to running task-oriented usability studies for your websites, web apps, or prototypes.

Article Summary

➡️Usability test based on tasks involves having users complete a task (or multiple) and observing their performance, issues, and success rate

➡️Tasks should correspond with your website’s user goal(s) and imitate real-life scenarios

➡️The test can be done on high-fidelity prototypes or live websites/apps

➡️To create the tasks use actionable verbs, provide a bit of context and keep it simple

➡️ Avoid leading instructions and task formulations that give away the answer

➡️You can set up a task-oriented usability test in 10 simple steps with UXtweak’s Website Testing tool, and get a detailed analysis

🐝  Register for a free account on UXtweak now and try it out!

What is task-based usability testing?

Your customers expect to easily navigate your website, get to the products they are looking for and find all the information they need. They expect an issue-free experience while using your website. This is where they often get disappointed. As we all know, confusing and bug-ridden websites expectedly lead to low conversions , high bounce rates, etc. 

task based usability testing

However, there are ways to combat this problem. Task-oriented usability studies combine qualitative research methods to provide you with in-depth explanations, and as much context as possible while not forgetting about important quantitative metrics and statistics. They take into account that your users need to accomplish specific goals on your website and they should be able to do so easily. 

A task-oriented usability study is built around a real-life task and scenario to simulate the real user experience and encourage users to interact with your product interface naturally. You can measure user success rate and test a website’s ability to do what it was built for – satisfying a customer from the user’s point of view and bringing conversions.

The task is simply an action you want your users to be able to complete in your interface. This type of user testing can be conducted in person or online with the help of specialized  usability testing tools .

Why you should try task-based usability tests?

Every website, web app, and mobile app is built with a goal and user goals in mind. For example, the main goal of an e-commerce website is to sell products, additional user goals could be to let users subscribe to the newsletter (to sell more products later on and create engagement).

When users experience issues stopping them from completing their goal it creates a bad brand experience , meaning your customers will be less likely to return to your store and create revenue.

task based usability testing

Unfortunately, even the most optimized or well-coded websites don’t get this aspect perfectly, as it is next to impossible to predict the exact way your actual users will be using your digital products. To battle this problem, you need to observe how your users interact with the interface and features in the context of the real day-to-day user experience.

Task-oriented usability testing allows you to qualitatively analyze how your users go about solving the tasks set by you , why users could not complete the test tasks successfully, or what distracted them during their efforts.

When is the right time to use task-based usability testing?

It is best to incorporate this type of usability test at more stages of development , to avoid developing a product that will later need changing.

When you can think about a specific usability testing scenario, you can create usability tasks. You can then conduct a task-oriented usability study on a high-fidelity prototype with a help of a Prototype Testing tool or conduct live Website Testing  or Mobile App Testing .

It is perfect for when you are looking for detailed answers from a larger number of your users to questions such as:  

  • Why does your e-commerce website have a high abandonment rate at the checkout?
  • Why don’t your users sign up for your newsletter?
  • What causes low conversions? 
  • Is your site navigation effective and intuitive? 
  • Can users find the information they are looking for about your company? etc. 

You can learn more about when to conduct usability testing in our Complete Guide to Usability Tests .

Easy Task-oriented Usability Testing with UXtweak

Test on prototypes, websites or apps and get actionable insights to improve your product! Clear reports, qualified testers and all with the most competitive pricing.

Advantages of remote task-based usability testing

  • Higher completion rate due to motivation to complete the task by setting relatable scenarios (for example: Subscribe to our newsletter.)
  • Testing on real day-to-day tasks that make it relatable to your users
  • Realistic results when put into practice correctly
  • Actionable insights to improve on 
  • Less costly than in-person interviews
  • Qualitative insights scalable to any number of users

Disadvantages of remote task-oriented usability tests

  • Poorly prepared usability tasks can lead to skewed data and even harmful results when changes are made based on the data
  • Unrelatable scenarios discourage users from completing tasks
  • Possible incorrect identification of goals 
  • Only makes sense to conduct on high-fidelity prototypes or finished products

How to create a task-based usability test?

Fi rstly, the most important part of well-structured task-oriented usability testing is setting the tasks correctly, so they are relatable to the testers and much easier to interpret. Creating great usability tasks is important in order to not collect skewed data. Let’s tackle that first.

Here you will find an example you can use as a stepping stone to creating your first study, making it easier for you to grasp the whole concept.

Example: Usability test for an e-commerce website

Start by setting the goals of your website. In this case, customers have to be able to order products, check and track their orders and get customer support without any difficulties. 

Some of the common mistakes e-commerce websites make that put customers off are:

  • counter-intuitive filtering and searching for products
  • inadequate payment methods
  • bad return policy and refunds
  • excessive load time of the website 
  • complicated shopping cart
  • long delivery time

To make sure you avoid these mistakes, we prepared these 3 sample tasks for you to start testing in no time.

Task 1: Find the least expensive smartphone in our offer and find more details about it.

Task 2: Find out whether it’s possible to use Paypal to pay in our online shop.

Task 3: Find how we can ship your order and which method is the least expensive.

task based usability testing

How do you write tasks for usability tests?

When writing usability tasks it’s important to  focus on real-life user scenarios and their interactions with your digital product.  Define a clear goal of what you’re trying to find out in your usability test and put together a list of usability testing tasks and questions that correspond to those goals. 

When you’re conducting your first usability test with the product a great tip is to focus your tasks on some of the most common actions users take with it. You can see that in the example above where we are testing an e-commerce website, the first task asks the user to find a specific product and details about it. Testing that user scenario is a priority for an e-commerce store as one of their main user goals is to get people to find and buy their products.    

Follow a short list of guidelines we’ve outlined below to create a perfect usability task that lacks bias.  Or check our guide to  creating usability testing tasks and questions .

Guidelines for writing usability testing tasks

  • Create simple realistic tasks – overly complicated tasks will lead to high abandonment rates
  • Set realistic scenarios – to increase relatability and motivation to complete the task
  • Use actionable verbs – the task has to encourage a user to carry out an action 
  • Scenarios must not guide or hint to users on how to complete it – the test will be useless if you tell your testers how to complete it
  • Leave out unnecessary pieces of information

As mentioned above, task-oriented usability testing is a powerful method when used correctly, especially in combination with pre and post-study questionnaires, think-aloud protocol, and crowd feedback.

Before conducting your first test, we recommend writing a  usability testing plan  to follow to make sure you don’t forget anything. 

It’s also good to have a working example to follow when writing tasks for your test. We gathered a couple of those to help you out. Choose the  usability testing template  that fits your needs.

Learn more about writing effective usability testing tasks in this quick YouTube video:

Let’s take a look at mistakes to avoid while writing tasks that make sense for the users, and engage them enough to carry them through your study without boring them to death. This is very important, since giving them non-engaging tasks may create several problems down the road and may create a study in which results are unusable in further research. 

How to write Task Scenarios for Usability Testing

If you’re looking to conduct a task-based usability study, it’s always good to have a working example to follow when writing tasks for your test. We gathered a couple of those to help you out. Choose the usability testing template that fits your needs.

Let’s take a look at mistakes to avoid while writing tasks that make sense for the users, and engage them enough to carry them through your study without boring them to death. This is very important, since giving them non-engaging tasks may create several problems down the road and may create a study in which results are unusable in further research. 

6 Mistakes to avoid when writing task scenarios for usability testing

1. getting too personal.

It is true that you need to understand the tester but be aware of the limitations of your relationship. You are the employer, they are the employee, and you should treat them as such. If you are asking personal questions or setting scenarios involving their loved ones, this could trigger an emotional response in the study participants, resulting in a biased study. Just follow the rule: “Let’s not bring my mother into this.”

❌ Bad example of a study question: You want to get a cake done for your mother’s birthday, buy her a cake.

✅ Good example of a study question: Get your colleague a present, due to her recent  promotion

2. Using dummy text to convey real information

Of course, when asking for their address or credit card information, use fake information, but make sure it’s realistic. Maybe you asked for their credit card information multiple times during their study, but if they simply used a fake text like “ 123 ,” they might not have noticed that you asked this question several times. Rather, when asking for their credit information use “ 0123 4567 8910 1112 ” instead.

❌ Bad example of a study question: Subscribe to our newsletter, use “ aaa ” as an email address

✅ Good example of a study question: Subscribe to our newsletter, use “ [email protected]

task based usability testing

3. Being overly specific

You should be creating a scenario, not a checklist for the user to pass. Being too specific in the questions you write may result in robot-like study results, where the participants simply go through the motions. Make the participant think and find the solution themselves, you shouldn’t point them in the right direction up front. Try to write your usability tasks without giving away the correct answer and avoid leading questions.

❌ Poor task example: Use the menu to access “ Contact ,” then click on the button labeled “ Contact Us ” and send us a message.

✅ Good example of a study question: Find a way to send us a message.

4. Keep it clear and simple

Scenarios need to be believable and need to reflect the situation the users would find themselves in. Let’s not add more information that would overwhelm the participant. Just ask the users to do what you need them to do, give a little bit of a perspective, provide context, and call it a day.

❌ Bad example of a study question: You have been very interested in our newsletter recently because our product is superior to our competitors. Please fill out the form to subscribe to our newsletter.

✅ Good example of a study question: You took interest in our newsletter, please subscribe to it.

5. Using your studies as a marketing tool

While on the topic of keeping to the point. For the love of all that’s holy, try to not bring marketing to your studies. Marketing-speak is a bunch of pretty words with no additional meaning or information. Use the user’s language. Marketing, which is highly based on emotions, should not be present in your qualitative, or quantitative research. These are two separate entities, and should not be mixed.

❌ Bad example of a study question: Take a look at our newest featured product and transcribe its endless possibilities. 

✅ Good example of a study question: Find the most recent addition to our product line, and repeat the positives stated on the page.

6. Asking about the future

The future is uncertain, you never know what will happen in the future, you might be rich, broke, or dead. Asking about the future will bring skewed results, therefore ask more about the past, or the present, rather than the future. 

❌ Bad example of a study question: Would you buy this product? 

✅ Good example of a study question: Do you have any prior experience with a product similar to ours?

10-step Usability Testing process

  • Register to UXtweak .
  • Create a new Website Testing Study from the dashboard.
  • Set the basic information – the study name, the domain you are going to conduct testing on, whether you want to protect your study by password, etc.
  • Integrate UXtweak snippet to your website – use Google Tag Manager for super quick and pain-free implementation. If you are not using GTM (you should start :), it is great!) just copy your snippet into your website code below <head> on every page you wish to record on. Participants can also test your website with a UXtweak Chrome Extension, without any installation to your website, or GTMs.
  • Set the start and success URL.
  • Create tasks and scenarios – just copy/edit our provided example if you want to start testing in a matter of minutes! If you need to write one yourself take a look at our explanation and guidelines above or visit our blog all about asking the right questions while testing
  • Set your options – UXtweak offers a lot more than just measuring task completion. You can find more about your options in Tasks Tab and how to use them here.
  • Prepare questionnaires and customize messages – UXtweak comes with already prepared messages and instructions, to save you time. They are fully customizable, so feel free to customize the messages you deem fit.
  • Finish the study setup – choose what information you want to collect, add your branding, set up a recruitment widget, and the setup is finished.
  • Recruit participants and you are ready to launch the test!

task based usability testing

💡Pro tip: There are many ways to get participants for your study, and some of them are even for free. Check our blog about recruiting participants for free , if you run a tight ship on a tight budget. 

Are you ready to take your website to the next level?

We’ve shown you how to set up a study, showed you an example, and listed all the benefits of using tasks in website testing. Still not sure about it? Why don’t you try it out for yourself, and see what happens when you listen to your users and adapt according to their needs.

With UXtweak you can test for these issues completely free.  Register now  and don’t miss out on the opportunity to make your website better.

Conduct Task-oriented Usability Tests with UXtweak

Easy 10-step setup process, intuitive UI, clear reports, qualified testers and all with the most competitive pricing.

People also ask (FAQ)

Task-based user testing is a type of user research where participants complete specific assignments using the tested product . These tasks mirror real-life scenarios and use cases and are used to point out any issues and improve the overall user experience .

Tasks in usability testing are specific activities or assignments that you want your participants to complete during the test . These tasks are typically based on common user goals and are used to measure the effectiveness, efficiency, and user experience of a product’s design .

It is important not to overwhelm your usability testing participants with too many tasks, because this could lead to a higher drop-off rate. It is recommended to include a maximum of 8 tasks, however, if the tasks are more complex 3-5 is better .

Tadeas Adamjak is Marketing Lead at UXtweak. His love for market research, working with data, and analytical mind, brought him to UXtweak where he puts these experiences into use. He has been with the company since its public launch and is in charge of ensuring customer satisfaction and getting the word out about UXtweak's cutting-edge products and services.  In addition to his marketing expertise, Tadeas is also an advocate for all things UX. He holds a Design Thinking certificate from a Google program and is currently pursuing his Master's degree in Marketing. 

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How to test large language models

Companies investing in generative ai find that testing and quality assurance are two of the most critical areas for improvement. here are four strategies for testing llms embedded in generative ai apps..

Checklist, checking boxes, testing, QA

There’s significant buzz and excitement around using AI copilots to reduce manual work, improving software developer productivity with code generators, and innovating with generative AI . The business opportunities are driving many development teams to build knowledge bases with vector databases and embed large language models (LLMs) into their applications.

Some general use cases for building applications with LLM capabilities include search experiences , content generation, document summarization, chatbots, and customer support applications. Industry examples include developing patient portals in healthcare, improving junior banker workflows in financial services, and paving the way for the factory’s future in manufacturing.

Companies investing in LLMs have some upfront hurdles, including improving data governance around data quality, selecting an LLM architecture , addressing security risks , and developing a cloud infrastructure plan .

My bigger concerns lie in how organizations plan to test their LLM models and applications. Issues making the news include one airline honoring a refund its chatbot offered , lawsuits over copyright infringement , and reducing the risk of hallucinations .

“Testing LLM models requires a multifaceted approach that goes beyond technical rigor, says Amit Jain, co-founder and COO of Roadz . “Teams should engage in iterative improvement and create detailed documentation to memorialize the model’s development process, testing methodologies, and performance metrics. Engaging with the research community to benchmark and share best practices is also effective.”

4 testing strategies for embedded LLMs

Development teams need an LLM testing strategy. Consider as a starting point the following practices for testing LLMs embedded in custom applications:

Create test data to extend software QA

Automate model quality and performance testing, evaluate rag quality based on the use case, develop quality metrics and benchmarks.

Most development teams won’t be creating generalized LLMs, and will be developing applications for specific end users and use cases. To develop a testing strategy, teams need to understand the user personas, goals, workflow, and quality benchmarks involved. 

“The first requirement of testing LLMs is to know the task that the LLM should be able to solve,” says Jakob Praher, CTO of Mindbreeze . “For these tasks, one would construct test datasets to establish metrics for the performance of the LLM. Then, one can either optimize the prompts or fine-tune the model systematically.”

For example, an LLM designed for customer service might include a test data set of common user problems and the best responses. Other LLM use cases may not have straightforward means to evaluate the results, but developers can still use the test data to perform validations. 

“The most reliable way to test an LLM is to create relevant test data, but the challenge is the cost and time to create such a dataset,” says Kishore Gadiraju, VP of engineering for Solix Technologies . “Like any other software, LLM testing includes unit, functional, regression, and performance testing. Additionally, LLM testing requires bias, fairness, safety, content control, and explainability testing.”

Once there’s a test data set, development teams should consider several testing approaches depending on quality goals, risks, and cost considerations. “Companies are beginning to move towards automated evaluation methods, rather than human evaluation, because of their time and cost efficiency,” says Olga Megorskaya, CEO of Toloka AI . “However, companies should still engage domain experts for situations where it’s crucial to catch nuances that automated systems might overlook.”

Finding the right balance of automation and human-in-the-loop testing isn’t easy for developers or data scientists. “We suggest a combination of automated benchmarking for each step of the modeling process and then a mixture of automation and manual verification for the end-to-end system,” says Steven Hillion, SVP of data and AI at Astronomer . “For major application releases, you will almost always want a final round of manual validation against your test set. That’s especially true if you’ve introduced new embeddings, new models, or new prompts that you expect to raise the general level of quality because often the improvements are subtle or subjective.”

Manual testing is a prudent measure until there are robust LLM testing platforms. Nikolaos Vasiloglou, VP of Research ML at RelationalAI , says, “There are no state-of-the-art platforms for systematic testing. When it comes to reliability and hallucination, a knowledge graph question-generating bot is the best solution.”

Gadiraju shares the following LLM testing libraries and tools:

  • AI Fairness 360 , an open source toolkit used to examine, report, and mitigate discrimination and bias in machine learning models
  • DeepEval , an open-source LLM evaluation framework similar to Pytest but specialized for unit testing LLM outputs
  • Baserun , a tool to help debug, test, and iteratively improve models
  • Nvidia NeMo-Guardrails , an open-source toolkit for adding programmable constraints on an LLM’s outputs

Monica Romila, director of data science tools and runtimes at IBM Data and AI , shared two testing areas for LLMs in enterprise use cases:

  • Model quality evaluation assesses the model quality using academic and internal data sets for use cases like classification, extraction, summarization, generation, and retrieval augmented generation (RAG).
  • Model performance testing validates the model’s latency (elapsed time for data transmission) and throughput (amount of data processed in a certain timeframe).

Romila says performance testing depends on two critical parameters: the number of concurrent requests and the number of generated tokens (chunks of text a model uses). “It’s important to test for various load sizes and types and compare performance to existing models to see if updates are needed.”

DevOps and cloud architects should consider infrastructure requirements to conduct performance and load testing of LLM applications. “Deploying testing infrastructure for large language models involves setting up robust compute resources, storage solutions, and testing frameworks,” says Heather Sundheim, managing director of solutions engineering at SADA . “Automated provisioning tools like Terraform and version control systems like Git play pivotal roles in reproducible deployments and effective collaboration, emphasizing the importance of balancing resources, storage, deployment strategies, and collaboration tools for reliable LLM testing.”

Some techniques to improve LLM accuracy include centralizing content, updating models with the latest data, and using RAG in the query pipeline. RAGs are important for marrying the power of LLMs with a company’s proprietary information.

In a typical LLM application, the user enters a prompt, the app sends it to the LLM, and the LLM generates a response that the app sends back to the user. With RAG, the app first sends the prompt to an information database like a search engine or a vector database to retrieve relevant, subject-related information. The app sends the prompt and this contextual information to the LLM, which it uses to formulate a response. The RAG thus confines the LLM’s response to relevant and contextual information.

Igor Jablokov, CEO and founder of Pryon , says, “RAG is more plausible for enterprise-style deployments where verifiable attribution to source content is necessary, especially in critical infrastructure.”

Using RAG with an LLM has been shown to reduce hallucinations and improve accuracy. However, using RAG also adds a new component that requires testing its relevancy and performance. The types of testing depend on how easy it is to evaluate the RAG and LLM’s responses and to what extent development teams can leverage end-user feedback.

I recently spoke with Deon Nicholas, CEO of Forethought , about the options to evaluate RAGs used in his company’s generative customer support AI. He shared three different approaches:

  • Gold standard datasets, or human-labeled datasets of correct answers for queries that serve as a benchmark for model performance
  • Reinforcement learning , or testing the model in real-world scenarios like asking for a user’s satisfaction level after interacting with a chatbot
  • Adversarial networks , or training a secondary LLM to assess the primary’s performance, which provides an automated evaluation by not relying on human feedback

“Each method carries trade-offs, balancing human effort against the risk of overlooking errors,” says Nicholas. “The best systems leverage these methods across system components to minimize errors and foster a robust AI deployment.”

Once you have testing data, a new or updated LLM, and a testing strategy, the next step is to validate quality against stated objectives.

“To ensure the development of safe, secure, and trustworthy AI, it’s important to create specific and measurable KPIs and establish defined guardrails,” says Atena Reyhani, chief product officer at ContractPodAi . “Some criteria to consider are accuracy, consistency, speed, and relevance to domain-specific use cases. Developers need to evaluate the entire LLM ecosystem and operational model in the targeted domain to ensure it delivers accurate, relevant, and comprehensive results.”

One tool to learn from is the Chatbot Arena , an open environment for comparing the results of LLMs. It uses the Elo Rating System , an algorithm often used in ranking players in competitive games, but it works well when a person evaluates the response from different LLM algorithms or versions.

“Human evaluation is a central part of testing, particularly when hardening an LLM to queries appearing in the wild,” says Joe Regensburger, VP of research at Immuta . “Chatbot Arena is an example of crowdsourcing testing, and these types of human evaluator studies can provide an important feedback loop to incorporate user feedback.”

Romila of IBM Data and AI shared three metrics to consider depending on the LLM’s use case.

  • F1 score is a composite score around precision and recall and applies when LLMs are used for classifications or predictions. For example, a customer support LLM can be evaluated on how well it recommends a course of action.
  • RougeL can be used to test RAG and LLMs for summarization use cases, but this generally needs a human-created summary to benchmark the results.
  • sacreBLEU is one method originally used to test language translations that is now being used for quantitative evaluation of LLM responses , along with other methods such as TER, ChrF, and BERTScore.

Some industries have quality and risk metrics to consider. Karthik Sj, VP of product management and marketing at Aisera , says, “In education, assessing age-appropriateness and toxicity avoidance is crucial, but in consumer-facing applications, prioritize response relevance and latency.”

Testing does not end once a model is deployed, and data scientists should seek out end-user reactions, performance metrics, and other feedback to improve the models. “Post-deployment, integrating results with behavior analytics becomes crucial, offering rapid feedback and a clearer measure of model performance,” says Dustin Pearce, VP of engineering and CISO at Amplitude .

One important step to prepare for production is to use feature flags in the application. AI technology companies  Anthropic, Character.ai, Notion, and Brex build their product with feature flags to test the application collaboratively, slowly introduce capabilities to large groups, and target experiments to different user segments.

While there are emerging techniques to validate LLM applications, none of these are easy to implement or provide definitive results. For now, just building an app with RAG and LLM integrations may be the easy part compared to the work required to test it and support enhancements. 

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Isaac Sacolick

Isaac Sacolick, President of StarCIO , a digital transformation learning company, guides leaders on adopting the practices needed to lead transformational change in their organizations. He is the author of Digital Trailblazer and the Amazon bestseller Driving Digital and speaks about agile planning , devops, data science, product management, and other digital transformation best practices. Sacolick is a recognized top social CIO, a digital transformation influencer, and has over 900 articles published at InfoWorld, CIO.com, his blog Social, Agile, and Transformation , and other sites.

The opinions expressed in this blog are those of Isaac Sacolick and do not necessarily represent those of IDG Communications, Inc., its parent, subsidiary or affiliated companies.

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Model Based Testing in Software Testing

Prerequisites: software-testing

Model-based testing is nothing but a simple testing technique in which we get different test cases that are described by the model. In this type, the test cases are generated via both online and offline test case models.  

Table of Content

Significance of Model-Based Testing

Types of model-based testing, advantages of model-based testing, disadvantages of model-based testing, real case scenario of a model.

In this case by considering the testing technique functionally we find out the model-based test cases. For checking the functionality of the software, the unit testing is not sufficient for this case so this is considered.

  • Early Defect Detection: Model-Based Testing (MBT) makes it possible for testers to find problems during the requirements or design phases by using model validation. By doing this, flaws are kept from spreading to more expensive fixing development phases.
  • Lower Maintenance Costs : Since test cases are derived from models, any modifications to the system can immediately update the appropriate test cases, which in turn can reflect any changes made to the models. This lessens the work and expense of maintaining test cases, particularly in complex and large-scale systems.
  • Reusable Test Assets : Models and test cases developed during the software development lifecycle can be utilized again for regression testing. This guarantees uniformity in testing procedures across projects and helps optimize the return on investment in testing efforts.
  • Encouragement of Agile and DevOps Methods: Because it facilitates quick feedback loops and continuous testing, it works well with Agile and DevOps approaches. As part of the CI/CD pipeline, test cases can be automatically developed and run, giving developers quick feedback and guaranteeing the quality of deliverables.
  • Enhanced Test Coverage: It generates test cases from models that describe all potential system behaviors and scenarios, assisting in ensuring thorough test coverage. This aids in the early detection of possible flaws throughout the development process.
  • Statecharts: These are an expansion of FSMs that enable complicated transitions, parallelism, and hierarchical state representation. They are frequently used to simulate the behavior of reactive systems, like embedded systems and user interfaces.
  • Markov Models: These systems display probabilistic behavior, with state changes taking place according to probabilistic rules. They are employed in system performance and reliability analysis as well as modelling stochastic processes.
  • Decision Tables: Decision Tables are a condensed, tabular method of expressing intricate decision reasoning. They are frequently utilized in rule-based systems and business logic validation, and they are helpful for modelling systems having conditional behavior.
  • Entity-Relationship Diagrams (ERDs) : These diagrams show how different entities in a database schema are related to one another. They are frequently employed in database design for showing the relationships and data structure between various entities.
  • Control Flow Graphs (CFGs) : CFGs show the order in which the code is executed, illustrating the control flow of a program. They are employed in test case generation, coverage analysis, and programme behavior analysis.
  • Data flow diagrams (DFDs): These show how data moves through a system with an emphasis on the entry, processing, and output of data. They are helpful in determining data dependencies and confirming that data transformations in software systems are accurate.
  • Unified Modelling Language (UML) diagrams : It offers a common notation for expressing different software system components. Use case diagrams show how users and systems interact, whereas activity diagrams show how control moves across a system.
  • Efficiency: The automation efficiency is so much higher in this type and the higher level also be acquired by the model.
  • Comprehensive testing is also possible in this type and the changes that have been made can be easily tested by model.
  • Different types of machines like finite state machines, unified model diagrams, and state charts are mostly taking part in this testing technique.
  • By reducing the cost of the process available in this type. Simultaneously many numbers of processes are running together for performance increase.
  • The defects that are made in the beginning stage are identified and the defect counts increase accordingly the testing undergoes in a progressing manner.
  • For testing purposes system always needs formal specifications and the changes are made according to different sets in a combined manner.
  • To understand the concept is so much difficult for the user and also for utilization. So, the learning curve of the model will be more i.e. the biggest failure of the model.
  • To overcome this situation, the model should be thoroughly improvised and trained.

When a user is ready to go through the web application then the user has multiple sections like Sign In, forgot password, and reset password options i.e. total 3 fields there to enter into the home page so that case is only for one user and when considering for model-based multiple users some permutation and combinations can be used to testing the product in a model type. So, the state transition diagrams are involved to fulfil the requirements of the user.  

 Multiple states with multiple transitions make it possible to reduce the complexity of the task that has been performed by different permutation and combination techniques. Validation of the test cases and state transition diagrams are created automatically and provide better solutions for many users present in a queue for requesting access to the specific model.

Model-based testing is an approach to evolutionary testing. The testers are involved in the testing type to form mental models that are coming on the paper for better readability and reusability of the product under testing. In the past study, the testing was manual, and automation for the recent study model-based testing came to market.

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Reasoning skills of large language models are often overestimated

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When it comes to artificial intelligence, appearances can be deceiving. The mystery surrounding the inner workings of large language models (LLMs) stems from their vast size, complex training methods, hard-to-predict behaviors, and elusive interpretability.

MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) researchers recently peered into the proverbial magnifying glass to examine how LLMs fare with variations of different tasks, revealing intriguing insights into the interplay between memorization and reasoning skills. It turns out that their reasoning abilities are often overestimated.

The study compared “default tasks,” the common tasks a model is trained and tested on, with “counterfactual scenarios,” hypothetical situations deviating from default conditions — which models like GPT-4 and Claude can usually be expected to cope with. The researchers developed some tests outside the models’ comfort zones by tweaking existing tasks instead of creating entirely new ones. They used a variety of datasets and benchmarks specifically tailored to different aspects of the models' capabilities for things like arithmetic, chess, evaluating code, answering logical questions, etc.

When users interact with language models, any arithmetic is usually in base-10, the familiar number base to the models. But observing that they do well on base-10 could give us a false impression of them having strong competency in addition. Logically, if they truly possess good addition skills, you’d expect reliably high performance across all number bases, similar to calculators or computers. Indeed, the research showed that these models are not as robust as many initially think. Their high performance is limited to common task variants and suffer from consistent and severe performance drop in the unfamiliar counterfactual scenarios, indicating a lack of generalizable addition ability.  The pattern held true for many other tasks like musical chord fingering, spatial reasoning, and even chess problems where the starting positions of pieces were slightly altered. While human players are expected to still be able to determine the legality of moves in altered scenarios (given enough time), the models struggled and couldn’t perform better than random guessing, meaning they have limited ability to generalize to unfamiliar situations. And much of their performance on the standard tasks is likely not due to general task abilities, but overfitting to, or directly memorizing from, what they have seen in their training data. “We’ve uncovered a fascinating aspect of large language models: they excel in familiar scenarios, almost like a well-worn path, but struggle when the terrain gets unfamiliar. This insight is crucial as we strive to enhance these models’ adaptability and broaden their application horizons,” says Zhaofeng Wu, an MIT PhD student in electrical engineering and computer science, CSAIL affiliate, and the lead author on a new paper about the research. “As AI is becoming increasingly ubiquitous in our society, it must reliably handle diverse scenarios, whether familiar or not. We hope these insights will one day inform the design of future LLMs with improved robustness.” Despite the insights gained, there are, of course, limitations. The study’s focus on specific tasks and settings didn’t capture the full range of challenges the models could potentially encounter in real-world applications, signaling the need for more diverse testing environments. Future work could involve expanding the range of tasks and counterfactual conditions to uncover more potential weaknesses. This could mean looking at more complex and less common scenarios. The team also wants to improve interpretability by creating methods to better comprehend the rationale behind the models’ decision-making processes. “As language models scale up, understanding their training data becomes increasingly challenging even for open models, let alone proprietary ones,” says Hao Peng, assistant professor at the University of Illinois at Urbana-Champaign. “The community remains puzzled about whether these models genuinely generalize to unseen tasks, or seemingly succeed by memorizing the training data. This paper makes important strides in addressing this question. It constructs a suite of carefully designed counterfactual evaluations, providing fresh insights into the capabilities of state-of-the-art LLMs. It reveals that their ability to solve unseen tasks is perhaps far more limited than anticipated by many. It has the potential to inspire future research towards identifying the failure modes of today’s models and developing better ones.” Additional authors include Najoung Kim, who is a Boston University assistant professor and Google visiting researcher, and seven CSAIL affiliates: MIT electrical engineering and computer science (EECS) PhD students Linlu Qiu, Alexis Ross, Ekin Akyürek SM ’21, and Boyuan Chen; former postdoc and Apple AI/ML researcher Bailin Wang; and EECS assistant professors Jacob Andreas and Yoon Kim. 

The team’s study was supported, in part, by the MIT–IBM Watson AI Lab, the MIT Quest for Intelligence, and the National Science Foundation. The team presented the work at the North American Chapter of the Association for Computational Linguistics (NAACL) last month.

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Model Validation and Testing: A Step-by-Step Guide

Here’s how to choose the right model for your data through development, validation and testing.

Peter Grant

In a previous article we discussed how to identify underfitting and overfitting, how these phenomena can lead to models that don’t match the available data and how to identify models that do fit the data well. These concepts can help you avoid major blunders and generate models that fit the data reasonably accurately. Now it’s time to think beyond accuracy and focus on precision . In this article, we’ll work to identify which of the possible models is the best fit for your data.

Back Up a Bit A Primer on Model Fitting

Model Validation and Testing

You cannot trust a model you’ve developed simply because it fits the training data well. The reason for this is simple: You forced the model to fit the training data! 

The solution: model validation. Validation uses your model to predict the output in situations outside your training data, and calculates the same statistical measures of fit on those results. This means you need to divide your data set into two different data files. The first is a training data set, which you use to generate your model, while the second is a validation data set, which you use to check your model’s accuracy against data you didn’t use to train the model.

7 Steps to Model Development, Validation and Testing

  • Create the development, validation and testing data sets.
  • Use the training data set to develop your model.
  • Compute statistical values identifying the model development performance.
  • Calculate the model results to the data points in the validation data set.
  • Compute statistical values comparing the model results to the validation data.
  • Calculate the model results to the data points in the testing data set.
  • Compute statistical values comparing the model results to the test data.

Let’s say you’re creating multiple models for a project. The natural choice is to select the model which most accurately fits your validation data and move on. However, now we have another potential pitfall. Simply because a model closely matches the validation data doesn’t mean the model matches reality. While the model in question performs best in this particular test, it could still be wrong.

The final step, and ultimate solution to the problem, is to compare the model which performed best in the validation stage against a third data set: the test data. This test data is, again, a subset of the data from the original data source. It consists only of points that were used in neither the model’s development nor its validation. We consider a model ready for use only when we compare it against the test data, and the statistical calculations show a satisfactory match.

Get More From Peter Grant What Is Multiple Regression?

Model Development, Validation and Testing: Step-by-Step 

This process breaks down into seven steps.

1. Create the Development, Validation and Testing Data Sets

To start off, you have a single, large data set. Remember: You need to break it up into three separate data sets, each of which you’ll use for only one phase of the project. When you’re creating each data set, make sure they contain a mixture of data points at the high and low extremes, as well as in the middle of each variable range. This process will ensure the model will be accurate at all ranges of the spectrum. Also, make sure most of the data is in the training data set. The model can only be as accurate as the data set used to create it, and more data means a higher chance of accuracy.

2. Use the Training Data Set to Develop Your Model

Input the data set into your model development script to develop the model of your choice. There are several different models you could develop depending on the data sources available and questions you need to answer. (You can find more information on the types of models in Data Science from Scratch .) In this phase, you’ll want to create several different models of different structures, or several regression models of different orders. In other words, generate any model that you think may perform well.

More From Built In’s Data Science Experts The Poisson Process and Poisson Distribution, Explained (With Meteors!)

3. Compute Statistical Values Identifying the Model Development Performance

Once you’ve developed your models, you need to compare them to the training data you used. Higher-performing models will fit the data better than lower-performing models. To do this, you need to calculate statistical values designed for this purpose. For instance, a common way to check the performance of a regression model is to calculate the r² value. 

4. Calculate the Model Results to the Data Points in the Validation Data Set 

In this step, you’ll use the validation data as input data for the model to generate predictions. Then you’ll need to compare the values predicted by the model with the values in the validation data set. Once complete, you have both the real values (from the data set) and predicted values (from the model). This allows you to compare the performance of different models to the data in the validation data set.

5. Compute Statistical Values Comparing the Model Results to the Validation Data

Now that you have the data value and the model prediction for every instance in the validation data set, you can calculate the same statistical values as before and compare the model predictions to the validation data set. This is a key part of the process. 

The first statistical calculations identified how well the model fit the data set you forced it to fit. In this case, you’re ensuring the model is capable of matching a separate data set, one that had no impact on the model development. Complete your statistical calculations of choice on each model, then choose the model with the highest performance.

Want More on Modeling? An Introduction to Bias-Variance Tradeoff

6. Calculate the Model Results to the Data Points in the Testing Data Set 

Use the test data set as input for the model to generate predictions. Only perform this task using the highest performing model from the validation phase. Once you complete this step, you’ll have both the real values and the model’s corresponding predictions for each input data instance in the data set.

7. Compute Statistical Values Comparing the Model Results to the Test Data

For the final time, perform your chosen statistical calculations comparing the model’s predictions to the data set. In this case you only have one model, so you aren’t searching for the best fit. Instead, you’re checking to ensure your model fits the test data set closely enough to be satisfactory.

Once you’ve developed a model that satisfactorily matches the test data set, you’re ready to start generating predictions. Don’t assume this means you’re done with model development completely, though; there’s a good chance you’ll eventually decide you need to tweak your model based on new available data sets.

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Gemma 2 vs Llama 3: Best Open-Source AI Model?

' src=

  • Google's newest Gemma 2 27B claims to be the best open-source model, despite being much smaller than Llama 3 70B.
  • In our tests, Gemma 2 shows great potential against Llama 3 but fizzles out in commonsense reasoning tests.
  • For a size that is almost 2.5x smaller, Gemma 2 27B indeed impressed me with its creative writing, multilingual ability, and perfect memory recall.

Gemma 2 vs Llama 3: Creative Writing

gemma 2 at creative writing

Multilingual Test

In the next round, I tried to understand how well both models handle non-English languages. Since Google touts that Gemma 2 is very good at multilingual understanding, I pitted it against Meta’s Llama 3 model. I asked both models to translate a paragraph written in Hindi. And well, both Gemma 2 and Llama 3 performed exceptionally well.

gemma 2 at multilingual test.

Gemma 2 vs Llama 3: Reasoning Test

gemma 2 reasoning test

Llama 3, on the other hand, has a strong reasoning foundation , most likely inferred from the coding dataset. Despite its small size — at least, in comparison to trillion-parameter models like GPT-4 — it showcases more than a decent level of intelligence. Finally, using more tokens to train the model indeed results in a stronger model.

Follow User Instructions

gemma 2 user following test

Gemma 2 vs Llama 3: Find the Needle

Both Gemma 2 and Llama 3 have a context length of 8K tokens, so this test is quite an apple-to-apple comparison. I added a huge block of text, sourced directly from the book Pride and Prejudice, containing more than 17,000 characters and 3.8K tokens. As I always do, I placed a needle (a random statement) somewhere in the middle and asked both models to find it.

gemma 2 memory recall test

Hallucination Test

gemma 2 hallucination test

I threw another (wrong) question to check the models’ factuality, but again, they didn’t hallucinate . By the way, I tested Llama 3 on HuggingChat as meta.ai browses the internet to find current information on relevant topics.

Gemma 2 vs Llama 3: Conclusion

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Title: learning to (learn at test time): rnns with expressive hidden states.

Abstract: Self-attention performs well in long context but has quadratic complexity. Existing RNN layers have linear complexity, but their performance in long context is limited by the expressive power of their hidden state. We propose a new class of sequence modeling layers with linear complexity and an expressive hidden state. The key idea is to make the hidden state a machine learning model itself, and the update rule a step of self-supervised learning. Since the hidden state is updated by training even on test sequences, our layers are called Test-Time Training (TTT) layers. We consider two instantiations: TTT-Linear and TTT-MLP, whose hidden state is a linear model and a two-layer MLP respectively. We evaluate our instantiations at the scale of 125M to 1.3B parameters, comparing with a strong Transformer and Mamba, a modern RNN. Both TTT-Linear and TTT-MLP match or exceed the baselines. Similar to Transformer, they can keep reducing perplexity by conditioning on more tokens, while Mamba cannot after 16k context. With preliminary systems optimization, TTT-Linear is already faster than Transformer at 8k context and matches Mamba in wall-clock time. TTT-MLP still faces challenges in memory I/O, but shows larger potential in long context, pointing to a promising direction for future research.
Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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ML Model Testing: 4 Teams Share How They Test Their Models

Despite the progress of the machine learning industry in developing solutions that help data teams and practitioners operationalize their machine learning models, testing these models to make sure they’ll work as intended remains one of the most challenging aspects of putting them into production. 

Most processes used to test ML models for production usage are native to traditional software applications, not machine learning applications. When starting a machine learning project, it’s standard for you to take critical note of the business, tech, and datasets requirements. Still, teams often neglect the testing requirements for later until they are either ready to deploy or altogether skip testing before deployment. 

Model Deployment Challenges: 6 Lessons From 6 ML Engineers

Best 8 Machine Learning Model Deployment Tools That You Need to Know

How do teams test machine learning models?

With ML testing, you are asking the question: “How do I know if my model works?” Essentially, you want to ensure that your learned model will behave consistently and produce the results you expect from it. 

Unlike traditional software applications, it is not straightforward to establish a standard for testing ML applications because the tests do not just depend on the software, they also rely on the business context, problem domain, dataset used, and the model selected. 

While most teams are comfortable with using the model evaluation metrics to quantify a model’s performance before deploying it, these metrics are mostly not enough to ensure your models are ready for production. You also need to perform thorough testing of your models to ensure they are robust enough for real-world encounters.

This article will teach you how various teams perform testing for different scenarios. At the same time, it’s worth noting that this article should not be used as a template (because ML testing is problem-dependent) but rather a guide to what types of test suite you might want to try out for your application based on your use case.

Developing, testing, and deploying machine learning models

Small sidenote

The information shared in this article is based on the interaction I had with team representatives who either worked on a team that performed testing for their ML projects or are still working with such groups. 

If you feel anything needs to be updated in the article or have any concerns, do not hesitate to reach out to me on LinkedIn .

1. Combining automated tests and manual validation for effective model testing

Organization.

GreenSteam – an i4 insight company

Computer software

Machine learning problem

Various ML tasks

Thanks to Tymoteusz Wolodzko , a former ML Engineer at GreenSteam, for granting me an interview. This section leverages both the responses gotten from Tymoteusz during the interview and his case study blog post on the neptune.ai blog. 

Business use case

GreenSteam – An i4 Insight Company provides software solutions for the marine industry that help reduce fuel usage. Excess fuel usage is both costly and bad for the environment, and vessel operators are obliged to get more green by the International Maritime Organization and reduce the CO2 emissions by 50% by 2050.

GreenSteam – an i4 insight company dashboard mock

Testing workflow overview

To perform ML testing in their projects, this team had a few levels of tests suites, as well as validation:

  • Automated tests for model verification ,
  • Manual model evaluation and validation .

To implement automated tests in their workflow, the team leveraged GitOps using Jenkins running code quality checks and smoke tests using production-like runs in the test environment . As a result, the team had a single pipeline for model code where every pull request was going through code reviews and automated unit tests.

The pull requests also went through automated smoke tests. The automated test suites’ goal was to make sure tests flagged erroneous code early in the development process.

After the automation tests were run and passed by the model pipeline, a domain expert manually reviewed the evaluation metrics to make sure that they made sense, validated them, and marked them ready for deployment.

Automated tests for model verification

The workflow for the automated tests was that whenever someone on the team made a commit, the smoke test would run to ensure the code worked, then the unit tests would run, making sure that the assertions in the code and data were met. Finally, the integration tests would run to ensure the model works well with other components in the pipeline.

Automated smoke test

Every pull request went through automated smoke tests where the team trained models and made predictions, running the entire end-to-end pipeline on some small chunk of actual data to ensure the pipeline worked as expected and nothing broke. 

The right kind of testing for the smoke suite can give any team a chance to understand the quality of their pipeline before deploying it. Still, running the smoke test suite does not mean the entire pipeline is guaranteed to be fully working because the code passed. So the team had to consider the unit test suite to test data and model assumptions.

Automated unit and integration tests

The unit and integration tests the team ran were to check some assertions about the dataset to prevent low-quality data from entering the training pipeline and prevent problems with the data preprocessing code. You could think of these assertions as assumptions the team made about the data. For example, they would expect to see some kind of correlation in the data or see that the model’s prediction bounds are non-negative.

Unit testing machine learning code is more challenging than typical software code. Unit testing several aspects of the model code was difficult for the team. For example, to test them accurately, they would have to train the model, and even with a modest data set, a unit test could take a long time.

Furthermore, some of the tests were erratic and flaky (failed at random). One of the challenges of running the unit tests to assert the data quality was that running these tests on sample datasets was more complex and took way less time than running them on the entire dataset. It was difficult to fix for the team but to address the issues. They opted to eliminate part of the unit tests in favour of smoke tests. 

The team defined acceptance criteria and their test suite was continuously evolving as they experimented by adding new tests, and removing others, gaining more knowledge on what was working and what wasn’t.

They would train the model in a production-like environment on a complete dataset for each new pull request, except that they would adjust the hyperparameters at values that resulted in quick results. Finally, they would monitor the pipeline’s health for any issues and catch them early.

GreenSteam MLOps toolstack

Manual model evaluation and validation

“We had a human-in-the-loop framework where after training the model, we were creating reports with different plots showing results based on the dataset, so the domain experts could review them before the model could be shipped.” Tymoteusz Wołodźko , a former ML Engineer at GreenSteam 

After training the model, a domain expert generated and reviewed a model quality report. The expert would approve (or deny) the model through a manual auditing process before it could eventually be shipped to production by the team after getting validation and passing all previous tests.

2. Approaching machine learning testing for a retail client application

Undisclosed

Retail and consumer goods

Classification

This team helped a retail client resolve tickets in an automated way using machine learning. When users raise tickets or when generated by maintenance problems, the application uses machine learning to classify the tickets into different categories, helping faster resolution.

This team’s workflow for testing models involved generating builds in the continuous integration (CI) pipeline upon every commit. In addition, the build pipeline will run a code quality test ( linting test ) to ensure there are no code problems. 

Once the pipeline generated the build (a container image), the models were stress-tested in a production-like environment through the release pipelines . Before deployment, the team would also occasionally carry out A/B testing on the model to evaluate performance in varying situations.

After the team deployed the pipeline, they would run deployment and inference tests to ensure it did not break the production system and the model continuously worked correctly.

Let’s take an in-depth look at some of the team’s tests for this use case.

Code quality tests

Running tests to check code quality is crucial for any software application. You always want to test your code to make sure that it is: 

  • Reliable (doesn’t break in different conditions),
  • Maintainable, 
  • and highly performant.

This team performed linting tests on their code before any container image builds in the CI pipeline. The linting tests ensured that they could enforce coding standards and high-quality code to avoid code breakages . Performing these tests also allowed the team to catch errors before the build process (when they are easy to debug).

A screenshot showing a mock example of linting tests

A/B testing machine learning models

“Before deploying the model, we sometimes do the A/B testing, not every time, depending on the need.” Emmanuel Raj, Senior Machine Learning Engineer

Depending on the use case, the team also carried out A/B tests to understand how their models performed in varying conditions before they deployed them, rather than relying purely on offline evaluation metrics. With what they learned from the A/B tests, they knew whether a new model improved a current model and tuned their model to optimize the business metrics better.

Stress testing machine learning models

“We use the release pipelines to stress test the model, where we bombard the deployment of the model with X number of inferences per minute. The X can be 1000 or 100, depending on our test. The goal is to see if the model performs as needed.” Emmanuel Raj, Senior Machine Learning Engineer

Testing the model’s performance under extreme workloads is crucial for business applications that typically expect high traffic from users. Therefore, the team performed stress tests to see how responsive and stable the model would be under an increased number of prediction requests at a given time scale. 

This way, they benchmarked the model’s scalability under load and identified the breaking point of the model. In addition, the test helped them determine if the model’s prediction service meets the required service-level objective (SLO) with uptime or response time metrics.

It is worth noting that the point of stress testing the model isn’t so much to see how many inference requests the model could handle as to see what would happen when users exceed such traffic. This way, you can understand the model’s performance problems, including the load time, response time, and other bottlenecks.

Testing model quality after deployment

“In production after deploying the model, we test the data and model drifts. We also do the post-production auditing; we have quarterly auditing to study the operations.” Emmanuel Raj, Senior Machine Learning Engineer

The goal of the testing production models is to ensure that the deployment of the model is successful and the model works correctly in production together with other services. For this team, testing the inference performance of the model in production was a crucial process for continuously providing business value. 

In addition, the team tested for data and model drift to make sure models could be monitored and perhaps retrained when such drift was detected. On another note, testing production models can enable teams to perform error analysis on their mission-critical models through manual inspection from domain experts.

An example of a dashboard showing information on data drift for a machine learning project in Azure ML Studio

3. Behavioural tests for machine learning applications at a Fin-tech startup

Natural language processing (NLP) and classification tasks

Thanks to Emeka Boris for granting me an interview and reviewing this excerpt before publication.

The transaction metadata product at MonoHQ uses machine learning to classify transaction statements that are helpful for a variety of corporate customer applications such as credit application, asset planning/management, BNPL (buy now pay later), and payment. Based on the narration, the product classifies transactions for thousands of customers into different categories.

MonoHQ

Before deploying the model, the team conducts a behavioral test. This test consists of 3 elements:

  • Prediction distribution,
  • Failure rate,

If the model passes the three tests, the team lists it for deployment. If the model does not pass the tests, they would have to re-work it until it passes the test. They always ensure that they set a performance threshold as a metric for these tests.

They also perform A/B tests on their models to learn what version is better to put into the production environment.

Behavioural tests to check for prediction quality

This test shows how the model responds to inference data, especially NLP models. 

  • First, the team runs an invarianc e test , introducing perturbability to the input data.
  • Next, they check if the slight change in the input affects the model response—its ability to correctly classify the narration for a customer transaction. 

Essentially, they are trying to answer here: does a minor tweak in the dataset with a similar context produce consistent output?

Performance testing for machine learning models

To test the response time of the model under load, the team configures a testing environment where they would send a lot of traffic to the model service. Here’s their process:

  • They take a large amount of transaction dataset,
  • Create a table, 
  • Stream the data to the model service,
  • Record the inference latency,
  • And finally, calculate the average response time for the entire transaction data.

If the response time passes a specified latency threshold, it is up for deployment. If it doesn’t, the team would have to rework it to improve it or devise another strategy to deploy the model to reduce the latency. 

“We A/B test to see which version of the model is most optimal to be deployed.”  Emeka Boris , Senior Data Scientist at MonoHQ.

For this test, the team containerizes two models to deploy to the production system for upstream services to consume to the production system. They deploy one of the models to serve traffic from a random sample of users and another to a different sample of users so they can measure the real impact of the model’s results on their users. In addition, they can tune their models using their real customers and measure how they react to the model predictions. 

This test also helps the team avoid introducing complexity from newly trained models that are difficult to maintain and add no value to their users.

4. Performing engineering and statistical tests for machine learning applications

FinTech – Market intelligence

Thanks to Laszlo Sragner for granting me an interview and reviewing this excerpt before publication.

A system that processes news from emerging markets to provide intelligence to traders, asset managers, and hedge fund managers. 

Arkera.ai LinkedIn cover image

This team performed two types of tests on their machine learning projects:

  • Engineering-based tests ( unit and integration tests ) ,
  • Statistical-based tests ( model validation and evaluation metrics ) . 

The engineering team ran the unit tests and checked whether the model threw errors. Then, the data team would hand off (to the engineering team) a mock model with the same input-output relationship as the model they were building. Also, the engineering team would test this model to ensure it does not break the production system and then serve it until the correct model from the data team is ready.

Once the data team and stakeholders evaluate and validate that the model is ready for deployment, the engineering team will run an integration test with the original model. Finally, they will swap the mock model with the original model in production if it works.

Engineering-based test for machine learning models

Unit and integration tests.

To run an initial test to check if the model will integrate well with other services in production, the data team will send a mock (or dummy) model to the engineering team. The mock model has the same structure as the real model, but it only returns the random output. The engineering team will write the service for the mock model and prepare it for testing.

The data team will provide data and input structures to the engineering team to test whether the input-output relationships match with what they expect, if they are coming in the correct format, and do not throw any errors. 

The engineering team does not check whether that model is the correct model; they only check if it works from an engineering perspective. They do this to ensure that when the model goes into production, it will not break the product pipeline.

When the data team trains and evaluates the correct model and stakeholders validate it, the data team will package it and hand it off to the engineering team. The engineering team will swap the mock model with the correct model and then run integration tests to ensure that it works as expected and does not throw any errors.

Statistical-based test for machine learning models

Model evaluation and validation.

The data team would train, test, and validate their model on real-world data and statistical evaluation metrics . The head of data science audits the results and approves (or denies) the model. If there is evidence that the model is the correct model, the head of data science will report the results to the necessary stakeholders. 

He will explain the results and inner workings of the model, the risks of the model, and the errors it makes, and confirm if they are comfortable with the results or the model still needs to be re-worked. If the model is approved, the engineering team swaps the mock model with the original model, reruns an integration test to confirm that it does not throw any error, and then deploy it.

Hopefully, as you learned from the use cases and workflows, model evaluation metrics are not enough to ensure your models are ready for production. You also need to perform thorough testing of your models to ensure they are robust enough for real-world encounters.

Developing tests for ML models can help teams systematically analyze model errors and detect failure modes, so resolution plans are made and implemented before deploying the models to production.

References and resources

  • MLOps at GreenSteam: Shipping Machine Learning [Case Study] – neptune.ai
  • Effective Testing for Machine Learning (Part I) (ploomber.io)
  • Effective Testing for Machine Learning (Part II) (ploomber.io)
  • Effective testing for machine learning systems. (jeremyjordan.me)
  • A Comprehensive Guide on How to Monitor Your Models in Production – neptune.ai
  • What is an A/B Test?. This is the second post in a multi-part… | by Netflix Technology Blog | Netflix TechBlog
  • Effective Testing for Machine Learning Projects – Eduardo Blancas | PyData Global 2021 – YouTube

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What are AI agents? 

The next big thing is AI tools that can do more complex tasks. Here’s how they will work.

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MIT Technology Review Explains: Let our writers untangle the complex, messy world of technology to help you understand what's coming next. You can read more from the series here.

When ChatGPT was first released, everyone in AI was talking about the new generation of AI assistants. But over the past year, that excitement has turned to a new target: AI agents. 

Agents featured prominently in Google’s annual I/O conference in May, when the company unveiled its new AI agent called Astra , which allows users to interact with it using audio and video. OpenAI’s new GPT-4o model has also been called an AI agent.  

And it’s not just hype, although there is definitely some of that too. Tech companies are plowing vast sums into creating AI agents, and their research efforts could usher in the kind of useful AI we have been dreaming about for decades. Many experts, including Sam Altman , say they are the next big thing.   

But what are they? And how can we use them? 

How are they defined? 

It is still early days for research into AI agents, and the field does not have a definitive definition for them. But simply, they are AI models and algorithms that can autonomously make decisions in a dynamic world, says Jim Fan, a senior research scientist at Nvidia who leads the company’s AI agents initiative. 

The grand vision for AI agents is a system that can execute a vast range of tasks, much like a human assistant. In the future, it could help you book your vacation, but it will also remember if you prefer swanky hotels, so it will only suggest hotels that have four stars or more and then go ahead and book the one you pick from the range of options it offers you. It will then also suggest flights that work best with your calendar, and plan the itinerary for your trip according to your preferences. It could make a list of things to pack based on that plan and the weather forecast. It might even send your itinerary to any friends it knows live in your destination and invite them along. In the workplace, it  could analyze your to-do list and execute tasks from it, such as sending calendar invites, memos, or emails. 

One vision for agents is that they are multimodal, meaning they can process language, audio, and video. For example, in Google’s Astra demo, users could point a smartphone camera at things and ask the agent questions. The agent could respond to text, audio, and video inputs. 

These agents could also make processes smoother for businesses and public organizations, says David Barber, the director of the University College London Centre for Artificial Intelligence. For example, an AI agent might be able to function as a more sophisticated customer service bot. The current generation of language-model-based assistants can only generate the next likely word in a sentence. But an AI agent would have the ability to act on natural-language commands autonomously and process customer service tasks without supervision. For example, the agent would be able to analyze customer complaint emails and then know to check the customer’s reference number, access databases such as customer relationship management and delivery systems to see whether the complaint is legitimate, and process it according to the company’s policies, Barber says. 

Broadly speaking, there are two different categories of agents, says Fan: software agents and embodied agents. 

Software agents run on computers or mobile phones and use apps, much as in the travel agent example above. “Those agents are very useful for office work or sending emails or having this chain of events going on,” he says. 

Embodied agents are agents that are situated in a 3D world such as a video game, or in a robot. These kinds of agents might make video games more engaging by letting people play with nonplayer characters controlled by AI. These sorts of agents could also help build more useful robots that could help us with everyday tasks at home, such as folding laundry and cooking meals. 

Fan was part of a team that built an embodied AI agent called MineDojo in the popular computer game Minecraft. Using a vast trove of data collected from the internet, Fan’s AI agent was able to learn new skills and tasks that allowed it to freely explore the virtual 3D world and complete complex tasks such as encircling llamas with fences or scooping lava into a bucket. Video games are good proxies for the real world, because they require agents to understand physics, reasoning, and common sense. 

In a new paper , which has not yet been peer-reviewed, researchers at Princeton say that AI agents tend to have three different characteristics. AI systems are considered “agentic” if they can pursue difficult goals without being instructed in complex environments. They also qualify if they can be instructed in natural language and act autonomously without supervision. And finally, the term “agent” can also apply to systems that are able to use tools, such as web search or programming, or are capable of planning. 

Are they a new thing?

The term “AI agents” has been around for years and has meant different things at different times, says Chirag Shah, a computer science professor at the University of Washington. 

There have been two waves of agents, says Fan. The current wave is thanks to the language model boom and the rise of systems such as ChatGPT. 

The previous wave was in 2016, when Google DeepMind introduced AlphaGo, its AI system that can play—and win—the game Go. AlphaGo was able to make decisions and plan strategies. This relied on reinforcement learning, a technique that rewards AI algorithms for desirable behaviors. 

“But these agents were not general,” says Oriol Vinyals, vice president of research at Google DeepMind. They were created for very specific tasks—in this case, playing Go. The new generation of foundation-model-based AI makes agents more universal, as they can learn from the world humans interact with. 

“You feel much more that the model is interacting with the world and then giving back to you better answers or better assisted assistance or whatnot,” says Vinyals. 

What are the limitations? 

There are still many open questions that need to be answered. Kanjun Qiu, CEO and founder of the AI startup Imbue, which is working on agents that can reason and code, likens the state of agents to where self-driving cars were just over a decade ago. They can do stuff, but they’re unreliable and still not really autonomous. For example, a coding agent can generate code, but it sometimes gets it wrong, and it doesn’t know how to test the code it’s creating, says Qiu. So humans still need to be actively involved in the process. AI systems still can’t fully reason, which is a critical step in operating in a complex and  ambiguous human world. 

“We’re nowhere close to having an agent that can just automate all of these chores for us,” says Fan. Current systems “hallucinate and they also don’t always follow instructions closely,” Fan says. “And that becomes annoying.”  

Another limitation is that after a while, AI agents lose track of what they are working on. AI systems are limited by their context windows, meaning the amount of data they can take into account at any given time. 

“ChatGPT can do coding, but it’s not able to do long-form content well. But for human developers, we look at an entire GitHub repository that has tens if not hundreds of lines of code, and we have no trouble navigating it,” says Fan. 

To tackle this problem, Google has increased its models’ capacity to process data , which allows users to have longer interactions with them in which they remember more about past interactions. The company said it is working on making its context windows infinite in the future.

For embodied agents such as robots, there are even more limitations. There is not enough training data to teach them, and researchers are only just starting to harness the power of foundation models in robotics. 

So amid all the hype and excitement, it’s worth bearing in mind that research into AI agents is still in its very early stages, and it will likely take years until we can experience their full potential. 

That sounds cool. Can I try an AI agent now? 

Sort of. You’ve most likely tried their early prototypes, such as OpenAI’s ChatGPT and GPT-4. “If you’re interacting with software that feels smart, that is kind of an agent,” says Qiu. 

Right now the best agents we have are systems with very narrow and specific use cases, such as coding assistants, customer service bots, or workflow automation software like Zapier, she says. But these are a far cry from a universal AI agent that can do complex tasks. 

“Today we have these computers and they’re really powerful, but we have to micromanage them,” says Qiu. 

OpenAI’s ChatGPT plug-ins, which allow people to create AI-powered assistants for web browsers, were an attempt at agents, says Qiu. But these systems are still clumsy, unreliable, and not capable of reasoning, she says. 

Despite that, these systems will one day change the way we interact with technology, Qiu believes, and it is a trend people need to pay attention to. 

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CMU, Meta Seek To Make Computer-based Tasks Accessible with Wristband Technology

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As part of a larger commitment to developing equitable technology, Carnegie Mellon University and Meta announce a collaborative project to make computer-based tasks accessible to more people. This project focuses on using wearable sensing technology to enable people with different motor abilities to perform everyday tasks and enjoy gaming in digital and mixed reality environments.

Meta’s research in electromyography uses sensors placed on the skin to measure the electrical signals the user generates through muscles in their wrist, which are translated into input signals for various devices. While Meta has already demonstrated that this technology could replace keyboards and joysticks, the team continues to invest and support different projects to confirm that this technology can be used by a wide range of people.

Douglas Weber (opens in new window) , a professor in the Department of Mechanical Engineering (opens in new window) and the Neuroscience Institute (opens in new window) at Carnegie Mellon University, has shown previously that people with complete hand paralysis retain the ability to control muscles in their forearm, even muscles that are too weak to produce movement. His team found that some individuals with spinal cord injury   still exhibit unique muscle activity patterns when attempting to move specific fingers, which could be used for human computer interactions.

“This research evaluates bypassing physical motion and relying instead on muscle signals. If successful, this approach could make computers and other digital devices more accessible for people with physical disabilities,” said Weber.  

Working with Meta, Weber’s team seeks to build upon their  initial results (opens in new window) to assess whether and to what extent people with spinal cord injury can interact with digital devices, such as computers and mixed reality systems, by using Meta’s surface electromyography (sEMG) research prototype and related software.

The project centers on interactive computing tasks. Approved by the Institutional Review Board, study participants begin by performing a series of adaptive mini games. Once their proficiency is benchmarked, the CMU team creates new games and other activities in mixed reality that are tailored to the abilities and interests of the participant.

“In the digital world, people with full or limited physical ability can be empowered to act virtually, using signals from their motor system,” explained Dailyn Despradel Rumaldo, a Ph.D. candidate at Carnegie Mellon University. “In the case of mixed reality technology, we are creating simulated environments where users interact with objects and other users, regardless of motor abilities.”

The project comes as an  ongoing research investment (opens in new window) by Meta to support the development of equitable and accessible interfaces to help people do more, together.

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What are the 7 steps of software testing?

The software testing process consists of seven steps: test plan creation, analysis of requirements, design of test cases, development of test scripts, execution of tests, bug fixes, and the last step is test completion which ensures all bugs are fixed and test summary reports are generated.

What are the 7 phases of SDLC?

The 7 phases of the Software Development Life Cycle (SDLC) include planning, requirement gathering, design, implementation (coding), testing, deployment, and maintenance. This approach guides the entire software development process, from initial project planning to ongoing support and improvement, ensuring efficient and high-quality software delivery.

What is STLC?

STLC stands for Software Testing Life Cycle, a structured software testing approach. It comprises various phases, including requirement analysis, test planning, test design, test execution, defect reporting and tracking, and test closure. STLC ensures that software testing is carried out efficiently, comprehensively, and in alignment with project goals, leading to higher-quality software products.

What is the SDLC and STLC?

The Software Development Life Cycle (SDLC) is a set of activities throughout the software development process. The Software Testing Life Cycle (STLC) is a set of actions throughout the software testing process.

What is entry and exit criteria?

The terms entry and exit criteria are commonly used in research and development but can be used in any sector. Benefits include ensuring that the process meets particular entry and exit criteria before moving on to the next level, including the last level before completion.

What does SDLC stand for?

SDLC stands for Software Development Life Cycle. It is a structured process that guides software development from inception to deployment.

What is the SDLC process?

The Software Development Life Cycle (SDLC) is a systematic approach used to develop software. It involves several stages, including requirements gathering, design, coding, testing, deployment, and maintenance. Each phase has specific activities and deliverables, ensuring a structured and efficient development process.

What is the design phase in the SDLC quizlet?

The design phase in the SDLC (Software Development Life Cycle) refers to the stage where the system’s architecture and specifications are planned and documented. It involves creating detailed technical designs and determining the best solution to meet the project’s requirements.

What is SDLC and its types?

The Software Development Life Cycle (SDLC) is a structured approach to developing software. It comprises various phases such as requirements gathering, design, development, testing, deployment, and maintenance. SDLC types include Waterfall, Agile, and DevOps, each with its own unique characteristics and methodologies.

Why is SDLC important?

The SDLC, or Software Development Life Cycle, is crucial as it provides a structured approach to developing high-quality software. It ensures effective project management, thorough requirements gathering, proper testing, and timely delivery, improving productivity, reduced costs, and customer satisfaction.

What is STLC in testing?

STLC, or Software Testing Life Cycle, is a series of testing activities conducted by a testing team to ensure software quality. It’s an integral part of the Software Development Life Cycle (SDLC) and encompasses diverse steps to verify and validate software for a successful release.

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Salman works as a Content Manager at LambdaTest. He is a Computer science engineer by degree and an experienced Tech writer who loves to share his thought about the latest tech trends.

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  10. Agile methodology testing best practices & why they matter

    QA teams are responsible for executing test plans. With agile testing they can sustainably deliver new features with quality. Learn best practices here.

  11. Software Testing Methodologies and Models

    Different testing methodologies help pinpoint several types of software errors. Knowing how each software testing model works is essential to building, deploying, and maintaining a high-quality testing strategy and software.

  12. 10 Top Model-based Testing Tools to Work With

    Model-Based Testing (MBT) uses graph models to design, automate, and execute tests. This blogs talks about top 10 model-based testing tools.

  13. What is Software Testing? The 10 Most Common Types of Tests Developers

    Software development and testing go hand in hand. And in the era of agile software development, with quick releases of small iterations, you should do testing more and more frequently. In order to perform effective testing, you need to know about the different types of testing and when you

  14. Guide To Test Approach: Different Types With Examples

    What is a Test Approach? A test approach is the implementation of the test strategy in a software project that defines how testers will carry out software testing, along with throwing light on strategy and execution to carry out different tasks. The testing approach also refers to the testing techniques, tools, strategies, and methodologies for testing any software product.

  15. Beyond Accuracy: Behavioral Testing of NLP Models with CheckList

    Inspired by principles of behavioral testing in software engineering, we introduce CheckList, a task-agnostic methodology for testing NLP models.

  16. Evaluating Large Language Model (LLM) systems: Metrics ...

    Additionally, numerous open-source test suites are available for this task, such as the Semantic Evaluation for Text-to-SQL with Distilled Test Suites ( GitHub ).

  17. Task-based Usability Testing + Example Task Scenario

    Guide for task-based usability testing: remote testing process, example task scenario + 6 mistakes to avoid.

  18. How to test large language models

    Automate model quality and performance testing. Once there's a test data set, development teams should consider several testing approaches depending on quality goals, risks, and cost considerations.

  19. Training on the Test Task Confounds Evaluation and Emergence

    We study a fundamental problem in the evaluation of large language models that we call training on the test task. Unlike wrongful practices like training on the test data, leakage, or data contamination, training on the test task is not a malpractice. Rather, the term describes a growing set of techniques to include task-relevant data in the pretraining stage of a language model. We ...

  20. Model Based Testing in Software Testing

    Model-based testing is an approach to evolutionary testing. The testers are involved in the testing type to form mental models that are coming on the paper for better readability and reusability of the product under testing. In the past study, the testing was manual, and automation for the recent study model-based testing came to market.

  21. Reasoning skills of large language models are often overestimated

    The study's focus on specific tasks and settings didn't capture the full range of challenges the models could potentially encounter in real-world applications, signaling the need for more diverse testing environments. Future work could involve expanding the range of tasks and counterfactual conditions to uncover more potential weaknesses.

  22. Model Validation and Testing: A Step-by-Step Guide

    Here's how to choose the right model for your data through development, validation and testing.

  23. Gemma 2 vs Llama 3: Best Open-Source AI Model?

    We have compared Gemma 2 and Llama 3, two top open-source models to evaluate how well they perform across a variety of tasks.

  24. Learning to (Learn at Test Time): RNNs with Expressive Hidden States

    The key idea is to make the hidden state a machine learning model itself, and the update rule a step of self-supervised learning. Since the hidden state is updated by training even on test sequences, our layers are called Test-Time Training (TTT) layers.

  25. Testing Methodologies: A Detailed Guide To Software Testing Methodologies

    Testing methodologies refer to systematic approaches and frameworks used in software development and quality assurance processes to assess the functionality, performance, and reliability of a software application or system. These methodologies encompass a set of principles, guidelines, and techniques that help organizations and testing teams plan, design, execute, and manage tests effectively.

  26. ML Model Testing: 4 Teams Share How They Test Their Models

    Learn about the testing practices of four machine learning teams and how they ensure the performance of their models.

  27. What are AI agents?

    They were created for very specific tasks—in this case, playing Go. The new generation of foundation-model-based AI makes agents more universal, as they can learn from the world humans interact ...

  28. Choice: Keeping pace with emerging models for generative AI in Life

    Embracing generative AI is imperative for life sciences organizations to stay competitive. However, the rapid evolution of models and data strategies has been overwhelming. While this diverse array of choices enhances business outcomes, life sciences leaders face the daunting task of strategizing for a future amid new AI breakthroughs emerging every week.

  29. CMU, Meta Seek To Make Computer-based Tasks Accessible with Wristband

    Carnegie Mellon University and Meta have announced a collaborative wearable sensing technology project to make computer-based tasks accessible to more people.

  30. What is the Software Testing Life Cycle (STLC)?

    This article on the Software Testing Life Cycle (STLC) discusses the fundamentals of software testing, its phases, methodologies, and best practices.