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Model Based AI Powered Test Automation-ACCELQ
Posted On: 28 October 2022

AI is a strategic priority for 83% of businesses. This technology has touched almost all aspects of business workflows, processes, and departments and is also transforming test automation. Today AI is lending its power to model-based test automation and adding greater velocity and confidence to automation initiatives.

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The ABC of Model-Based Test Automation

Software testing has become increasingly challenging as applications are becoming more complex. Since software literally runs the world, its functionalities, workflows, external interfaces, etc., are also becoming increasingly complex. Multiple flows and interfaces emerge because of complex functionality structures, and the chance of missing something in the testing process becomes higher.

Model-based test automation is an efficient way to add velocity and scalability to testing. It allows organizations to increase the breadth and depth of testing software applications by enabling them to employ application models to generate test cases automatically.

This testing approach employs a system’s model under test to generate test cases. This model could be static or dynamic. The tests are generated automatically from the model or semi-automatically with some user input. Most static models are used for GUI testing. Dynamic models work well for API testing.

Exploring the Advantages

Model-based testing bodes well for testing complex systems with many possible states or behaviors. The model-based approach:

  • Reduces testing time and increases testing footprint while reducing the time to create, maintain and prioritize test cases
  • Increases test accuracy and allow testing teams to reuse test cases
  • Increases testing velocity and accuracy in agile and DevOps environments
  • Reduces difficulty of testing complex applications

Model-based test automation works well because model creation is a part of the software development process. Unlike test automation, the model-based test approach does not need to indulge in independent script development for testing. This testing approach allows developers and testers the flexibility to focus on the models created to cover system requirements only, for example, and build a testable application from the start.

Testers, business analysts, or developers - anyone can create models to cover any level of requirements. Models can be created from business logic to user stories and linked to each other. Once the models are built, test cases can be generated automatically.

The tests can also be updated automatically post any changes to the model. These testing tools also help users identify new tests, updated tests, and tests that have become obsolete due to changes in the model.

Since model-based automation adopts the codeless approach, it promotes reusability and scalability of test assets across the digital landscape.

Introducing AI to Model-Based Test Automation

AI-powered model-based test automation is essentially test automation on steroids. AI has made significant headways in the testing space by delivering self-healing tests and neural network capabilities that deliver greater test resilience; AI is steadfastly influencing test automation.

Introducing AI to model-based test automation platforms allows teams to leverage the power of AI to power up model-based testing. AI works in tandem with the model-based automation platform and extends its capabilities to it. It allows teams to expand the test automation coverage even more and increase their testing footprint effortlessly.

With AI, model-based test automation shifts testing left, allowing users to test earlier in the sprints. It also extends automation testing capabilities to both modern and legacy technologies. It allows teams to amplify end-to-end automation testing by helping users visually construct robust IDs and explore elements without analyzing the DOM.

AI can also work with composite controls in UI frameworks such as Kendo, Bootstrap, Google MD, and others and deliver execution reliability with autonomics-based self-healing and exception handling.

More profoundly, merging AI with model-based testing allows organizations to:

Increase Testing Footprint and Reliability

Testing teams are under increased pressure to minimize testing effort while maximizing testing coverage to eliminate risks without compromising testing velocity. AI-powered model-based test automation gives users smart test case designs and a centralized area to design and maintain data-driven test scenarios.

While model-based testing allows organizations to design test cases without writing code, powering it with AI delivers the capacity to auto-generate tests based on numerous scenarios and business logic.

AI enables visual and intuitive element identification. It can complement the codeless platform in its ability to handle iframes and other dynamic controls and help create logical and business-driven test plans as well. Comprehensive and more reliable testing then becomes the natural outcome of these efforts.

Support Advanced Interactions and Logic Development

AI-powered model-based test automation also supports advanced interactions and logic development. It ensures that codeless capabilities can be extended to test complex and business-impacting enterprise applications. It ensures that the codeless tools do not oversimplify testing challenges and avoid addressing real-world complexities.

With AI, model-based test automation plans can be employed for verifying elements like Dynamic web pages, database validations, and APIs. Middleware automation also becomes simpler and easier to maintain.

Ensure Efficient Test Data Management

Agile and DevOps teams are working across distributed environments today. Maintaining test data in a coherent and easily discoverable format can become a challenge for end-to-end testing across applications and architectures.

Manual test data management is not only resource-intensive but is also error-prone. Test data management becomes much simpler with model-based test automation. When added to this mix, AI enables smarter provisioning, management, and tracking and provides detailed visibility into the test data.

The technology also ensures that it tracks the test data each time it is altered throughout end-to-end tests, thereby ensuring greater traceability across all test assets. This traceability ensures that test assets always stay in sync. It also drives cost savings of almost 60% by providing automated change impact analysis and alert management.

Facilitate Intelligent Automation for Higher Velocity and Improved Insights

Testing and development teams need greater testing velocity. AI enables this by giving model-based test automation self-healing capabilities and the capacity to intelligently adapt to unexpected application changes.

Robust element ID based on AI ensures a reliable test execution. Teams do not need to look for technical identifiers owing to the element ID capabilities. They also do not spend time refactoring thousands of scripts, which otherwise only contributes to high maintenance costs.

Model-Based or AI-Powered Test Automation - The Verdict

Testing must become highly efficient and operate as a part of the development process as approaches like DevOps become standard in software development. In these environments, testing must be continuous as inefficiencies are unaffordable.

AI-powered model-based test automation makes testing more democratic, allows business users to act as testers, and allows enterprises to keep up with the frequently changing application landscape by automatically updating impacted test cases and auto-fixing false positives. Three times the productivity and 70% cost savings emerge as outcomes of using the right AI-powered model-based test automation platform.

Check out ACCELQ’s industry-first autonomics-based automation platform to inject quality and confidence into your testing process. Connect with us today!

Victor liu IMG

Victor Liu

Principal Solutions Architect

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