AI (artificial intelligence) technology is evolving at breathtaking speed — its application in the QA testing industry continues to change as well, with no final landing point in sight. At this point, many automation tools offer built-in AI and ML (machine learning) functionality, and QA testing teams use them to enhance testing operations, including test development, automated script creation, maintenance, and test data generation.
For experienced testing teams, this means incorporating new processes to use AI/ML to their advantage. AI/ML technology in test automation tools can assist testing teams in generating test scenarios from user stories or requirements, and even generate test automation automatically with follow up monitoring of results for errors.
Identifying test scenarios for test development
Test development starts with reviewing user stories or requirements. Testers then develop test cases for each scenario that verify the acceptance criteria or requirements. Testers may also develop tests around related functionality to prevent defects from turning up in nearby features. Test development for manual or automated testing is a time-consuming process.
Why not use AI to run predictive analysis against the user stories and requirements? Predictive analytics (PA) finds patterns in large data sets and can create test scenarios in the areas that are most prone to defects, and is useful for discovering end user behavior. AI technology generates the test cases, saving time and effort. An experienced tester and product SME may need to review tests for accuracy, but reviewing is faster than generating tests from scratch.
AI enhances test automation development in key ways, including:
- Analyzing data to detect usage patterns and generating test cases in critical areas.
- If requirements change mid-sprint, the tests are not lost, but can be modified automatically.
- Leveraging production data to provide AI information on user behavior to discover common workflows.
- Generating tests for areas that may not be known or accessible to human testers.
Testing teams can use AI to assign priorities to test cases and ensure that critical business functions are fully covered. AI can set the priority based on the frequency of use for the function within an application and help balance test suites by prioritizing and ensuring testing covers essential functions. Consider using AI to optimize testing by generating critical tests, prioritizing, and creating test suites that match customer usage data.
Improving script accuracy when creating test scripts
Codeless and low-code test automation tools use NLP (Natural Language Processing) to translate written test scripts into test automation. When a user records an automated test, the system creates a written script and then translates it into code.
As testers record test automation, tools often open messages for the user to address if the AI system has a suggestion, hits an error, or detects a defect. Some tools provide tips and suggestions to aid in script creation. When testers execute a recorded test, they observe it and check for any messages from the system suggesting improvements or changes.
Test automation tools with AI also create visual regression testing scripts. Visual testing is where testers compare the visual elements on each page to the past release to verify nothing has changed that isn’t documented.
Visual testing finds cosmetic bugs that negatively impact the user experience, and when manually performed can be a time-consuming process unless the tester leverages AI. These tools can automatically execute visual tests in minutes by comparing code statements and pixel locations. Additionally, testing tools can flag defects that testers either approve for defect entry or mark to ignore.
Visual testing requires allowing the AI model to capture the document object model (DOM) prior to creating or executing tests. Once the DOM elements are known, that data becomes the baseline that the tool uses to detect changes in visual elements. Using AI to perform UI testing for changes saves extensive testing time and frees up testers to focus on testing critical areas or backend processes not covered by AI.
Leverage self-healing for automated test maintenance
The best part of AI technology is the self-healing option for test script maintenance. This self-healing technology identifies code and UI layer changes to automatically edit failed tests after execution. It’s like having the system perform failure analysis and fix the script at the same time.
The time a human tester would spend debugging or analyzing the failure, retesting, and then correcting the scripts is considerable — it’s the reason many organizations have abandoned test automation projects. When applications change frequently, keeping the scripts updated takes far too long. Using self-healing technology significantly reduces automated test maintenance time.
Once AI generates and executes an automated test, it knows which objects are associated with the test and where objects are in the UI. It can detect differences and edit scripts without human intervention. Human testers may have to review changes to ensure accuracy, but the time saved on test maintenance helps increase the ROI for test automation.
Generating test data with AI
Last but not least, AI generates test data. AI test automation tools can create data for test execution per test, server, or environment. The best part is it takes only minutes, and is repeatable. After learning patterns and characteristics from actual data, AI can generate test data that is secure and realistic.
Leveraging AI eliminates the time needed by teams to create test data manually, a direct benefit to clients. The same applies to having to request data from development or IT that takes resources away from coding and deployment. Using AI-generated test data does take some time to learn, but once it’s going, it saves time and increases testing effectiveness.
AI technology in test automation tools is just getting started, and will change and improve, making these basic practices easier and faster. While it cannot replace human testers or coders, AI is ready to be the tireless teammate that handles all the laborious and tedious grunt work for a testing team. Leverage AI for test automation now by generating test scenarios, automated test cases, and test data. Take advantage of self-healing technology to reduce automated test maintenance and improve testing value.
