Software QA is often seen as an expensive necessity for any development team; testing is costly in time, manpower, and money, while still being an imperfect process prone to human error. By bringing artificial intelligence and machine learning into the testing process, we not only expand the scope of what can be tested, but also automate much of the testing process itself.

This reduces the time and money spent on testing without sacrificing scope or quality, enabling engineering teams to break out of the “iron triangle” of project management. Read on to learn how applying AI and machine learning to the testing process has the potential to revolutionize the testing environment.

test questions

Manual testing involves the development of test suites, and the generation of test data to be used with the tests. While automatically running a test suite on commit can be easily integrated into the development pipeline to prevent deploying failing code, the test suite itself is still only as good as the test cases it contains and the test data used. The developers or testers who create the tests are humans. This means mistakes can be made and test cases can be missed. As software grows, the number of tests will inevitably increase, making it harder to stay on top of the test suite and ensure good code coverage.

These challenges can be overcome by introducing artificial intelligence into the testing process. AI can be applied in a variety of ways in a project, from scraping software to automatically generating test suites using test data, to visually analyzing software output to find bugs that are not easily discovered by traditional functional testing.

visual test

Image-based learning algorithms can be trained to analyze user interfaces, enhancing the testing process to help ensure that everything on a web page displays correctly. This can be done with fewer bugs than traditional functional testing and much faster than manual testing. This saves time and money because functional tests for UI validation are time-consuming to develop and quickly become very verbose, making them difficult to maintain.

Assertions powered by Vision AI have revolutionized the way UI functional tests are written by significantly reducing the amount of code required to test assertions. For example, instead of writing lengthy code to inspect DOM elements, AI-powered assertions analyze the output against the target expected output (usually a screenshot). If the output matches the screenshot, the test passes. This immediately highlights differences and also makes it easier to write better tests that can be applied to different devices and screen resolutions.

AI spiders and user analytics

Generating user journeys takes a lot of time, and in sufficiently mature software, the number of possible user journeys quickly approaches beyond the number that can be covered by manually created test cases. AI crawlers are used to automatically discover applications and can be used in conjunction with other testing procedures, such as regression testing, to quickly find bugs introduced in the user journey. It involves leveraging machine learning to create a model that can navigate an application by interacting with UI elements. The model creates a series of paths through the application to automatically generate working patterns from which tests can be written. These tests compare the current mode to the expected working mode to highlight differences as part of the testing process.

AI crawlers can be replaced or supplemented with ML-enabled usage analytics. By observing and understanding how end users use the application, the test system can identify the test cases that the user actually traverses, rather than all possible paths through the application, greatly reducing the number of tests required to provide full quality assurance.

no-code testing

Using record and playback interfaces is a popular way to generate tests, but they can be difficult to maintain as UI elements change. AI-driven no-code testing opens up the possibility of creating self-healing test cases that require little maintenance. AI can enhance the recording and playback process by dynamically generating object locators when interacting with it. All commands, from mouse clicks to keyboard input, and object types, whether it may be a drop-down option, an input field, or something else, are recognized.

AI-driven no-code tests are self-healing. By developing the object model on the page, it is able to rediscover UI element locators that have been moved or changed in some way without requiring manual reconfiguration. This can save a lot of time when developing UI/UX tests, which often require constant maintenance to adapt to changes.

Continuous verification

Instead of defining tests as a specific stage in the CI/CD pipeline, continuous verification allows tests to be run at all stages of the development process using continuous verification. AI-driven continuous validation can automatically risk assess new releases by tracking thousands of metrics at every stage of the development process, much faster than manually processing machine log data.

These risk assessments can be used as part of an automated decision-making deployment process. If a deployment is determined to be too risky, AI can be used to automatically roll back or roll forward the deployment to prevent unstable code from remaining in production. Not only do these save an emergency call to a technologist at 3am, but the AI ​​is also capable of error diagnosis and classification, so errors, warnings and exceptions can be automatically understood and classified based on severity, further reducing errors that rely on experts to determine risk levels .

The future of artificial intelligence and software testing

Many potential applications of AI- and ML-based software testing methods are still in their infancy, and they are not widely used in the software testing industry. These testing methods are new, but AI-driven testing methods demonstrate the potential to expand not only the scope of testable within software, but also the automation of the testing process. Software QA is one of the most expensive parts of the development process, so the potential cost savings in time and money are huge for the development team.

Reviewing Editor: Guo Ting

Leave a Reply

Your email address will not be published.