Artificial intelligence (AI) has changed and will continue to change QA, which means that we as testers need to be ready. AI-powered testing tools will automate repetitive tasks, predict problems, and improve efficiency and accuracy. It creates more time for creative tasks, but at the same time places new demands on the tester. Those who have the ability to work with systems based on AI and benefit from testing with AI will be in demand. Test automation in the future is not about pitting testers against AI, but rather how to test using AI.
Here are some ways testers can benefit from AI in their work.
With AI, it is possible to generate test cases in different ways and a common method is to use Machine Learning (ML). Machine learning is a type of AI that enables computers to learn from data without being explicitly programmed. In testing, machine learning can be used to learn about the software and its behavior. By analyzing the code, AI can identify potential sources of errors. These error sources can then be used to generate test cases designed to find potential errors before they occur.
Another branch of AI that enables the interaction between humans and computers to mimic natural language as much as possible is Natural Language Processing (NLP). The use of NLP means that testers do not have to learn new rules or syntax for different programming languages. The creation and maintenance of test scripts can be lengthy and time-consuming, but with scriptless automation through NLP, the scripts are written in natural language and one of the advantages is the low learning curve. Unlike most automation tools that require some form of programming knowledge. An additional benefit is increased understanding and readability that allows all roles within the team to review the test cases as needed. Because plain English can be used, it becomes possible for everyone on the team to create test cases, and product owners or project managers can step in and increase test coverage and quality.
By analyzing user documentation and requirements, AI can identify potential user scenarios. These user scenarios can then be used to generate test cases that simulate how users will use the software. As ML learns from previous test runs, analyzes results, and finds crucial patterns, it will be possible to find repetitive or less important test cases and improve the test suite.
Requirements such as delivering a consistent user experience regardless of whether a user has Firefox on their laptop or Chrome on their smartphone are not uncommon. And further, there is a growing need for users to access applications on various devices such as tablets and wearables. For testers, this can mean an inhuman number of UI test cases on all browsers and devices. It makes it impossible to keep up and the need for visual AI verification can become a suitable solution.
Visual AI testing involves generating, analyzing, and comparing images to detect possible changes in pixel values, known as “visual diffs”. A test runner is needed to run visual tests in combination with a browser automation framework to mimic user behavior. Screenshots are captured at specified times and used as expected results in future comparisons. During the test run, any possible changes trigger a screenshot and are compared against the reference image. In case of deviation, the test is flagged as failed and an automatic report is generated for QA to review.
Self-healing and adaptive testing
AI-powered test automation can be self-healing, meaning it can adapt to changes in the software or test environment without requiring manual intervention. This is valuable in agile development environments where changes occur frequently. Failed tests are discovered during the testing process, and many fail due to poor maintenance. This means that QA spends time fixing test bugs that could be spent fixing software bugs instead. Manual test maintenance can also delay product development and launch. Self-healing test automation reduces (or even eliminates) manual test maintenance. This leads to fewer failed tests and a more up-to-date view of test status.
Too often tests fail due to changes to objects or their properties in the user interface. This is where the AI-powered self-healing modus operandi comes in handy. Objects can be detected even after their properties have been changed, effectively eliminating the need to modify the script.
Instead of failing the test, the AI automatically edits the script to fit the new object properties. The self-healing feature scrapes, evaluates, selects, and edits the script as needed within a fraction of a second. This saves time and adds another layer of automation to further reduce manual intervention.
AI is under development and has great potential to take over the “traditional” tasks of the tester such as creating, running, and maintaining test cases. AI has the ability to analyze large amounts of data and contribute comprehensive test scenarios that lead to higher levels of coverage and better identification of potential bugs. Accuracy and speed increase, leading to fewer bugs and faster releases. The new era of AI-based testing leads to increased production and takes some of the workload off the tester. In the future, testers will not “just” be testers, but should develop into strategic thinkers with a deep understanding of both technical aspects and the requirements of the business.
It may appear that the tester will be replaced in the future, but the human skills should not be underestimated. AI is only as good as the data it has been trained on and cannot replace human intuition or creativity. Sure, it’s promising when it comes to automating repetitive tasks, but it can’t replace the critical thinking and problem-solving skills the tester possesses. AI cannot contribute the emotional intelligence required to understand the product from a user perspective and relate to the experience. This limitation means that the AI can miss errors that lead to a negative impact on the user experience. The future of QA lies in finding the right balance between AI and human testing where AI-based tools assist the tester in completing tasks more efficiently and accurately. Testers willing to learn about AI and invest in AI-powered testing tools will be well positioned to succeed in the future of test automation.
About the author
Paul Singh is an analytical problem solver with a keen interest in technology who enjoys working in teams as he likes to interact with other people. Among other things, Paul has worked with exploratory tests against mobile devices and the web as well as with automation in C# and selenium.