Is it worth implementing artificial intelligence in testing?

The artificial intelligence approach in software testing is a powerful tool that improves efficiency more than traditional automation.

As far as scenarios are concerned, the artificial intelligence mentioned here refers to its modern state, not an ideal goal. We live in a world of narrow AI, or weak AI, that beats humans at individual tasks, such as troubleshooting basic bugs faster than developers. But we are still years or decades away from truly powerful AI that can do almost anything a human can do. This means that AI testing will not be conducted without human input, but the workload can be minimized.

How can artificial intelligence implementation improve the software testing process?

Artificial intelligence in software testing is a natural evolution of automated testing. AI test automation goes a step further than simulating human work. AI also decides when and how to run tests in the first place.

The innovation doesn’t stop there. Artificial intelligence testing has become a reality. Depending on the implementation, tests will be modified or created from scratch without any human input. If the complexity of the project leaves people wondering how to test, this is a great solution - artificial intelligence may well be the answer.

Benefits of Artificial Intelligence

This section alone has a series of articles based on definitions and other factors. Let’s stick to the benefits of AI testing and other uses of AI in testing.

•AI automated testing can save time. Scheduling wonders can be achieved using test automation tools, but you can take it to the next level. What if it was possible to maintain only useful tests? For example, tests could be automatically canceled or paused to investigate whether they are indeed a waste of time.

•Testing consistency improves accuracy. It's natural to occasionally encounter tests that fail for no apparent reason. Such tests can be automatically flagged for AI review to identify coding issues or point out conceptual flaws discovered across multiple tests.

•Test maintenance becomes less cumbersome. This is especially important for B2C solutions that often adjust their user interface for A/B purposes on a daily basis (if not more frequently). For tests that mimic the user journey, small changes like this can still be disruptive, for example, a button doesn’t exist at all. Combined with artificial intelligence, test automation means tests can adapt to user interface (UI) changes without the need for human input.

AI Testing Best Practices

Here are some recommendations from trial and error from vendors at the forefront of AI testing.

•Know what you are getting into. Pushing test automation without adequate preparation is a huge time sink. Just like automated testing, a lack of senior experts who can lead the way can be disastrous.

•Put your test suite together. Missing or incorrect tags, spelling errors, and legacy databases can all skew the data that AI will use to improve testing.

•Write down your goals for implementing AI. This includes the business goals you want to solve (e.g., significantly improve retention through a smoother user experience), testing goals to verify if the AI ​​effort is worth the effort, and some human effort to see if you're on the right track. Smart test benchmarks.

•Alert colleagues. Incorporating artificial intelligence into testing is a lengthy process that may impact the availability of testing experts and their output in the shortest possible time. Your project managers, product owners, and upper management will appreciate advance notice of this drastic change. Of course, developers should know this too, especially if they handle unit testing for their project.

•Ensure test management is equally innovative. AI testing is of little use if your team still insists on testing on Excel. There is a need for a dedicated test management solution that is friendly to third-party AI tools.

Software testing automation method based on artificial intelligence

The method of integrating artificial intelligence into software testing mainly comes from the most popular artificial intelligence technology. They are machine learning, natural language processing (NLP), automation/robotics and computer vision. Here are some examples of how these techniques can be used for testing.

•Pattern recognition employs machine learning to find patterns in a test or test execution that can be turned into actionable insights. If an issue of the same class causes multiple tests to fail, the AI ​​solution will ask the team to revisit the potentially problematic code. Pattern recognition can also be used in the software code itself to discover and predict potential vulnerabilities.

•If automated tests start to cause headaches, self-healing can correct them. Unstable testing can ultimately be traced back to the path of the problem. Defects that appear irreproducible will be caught and resolved. As projects get bigger, self-healing tests will be a real game-changer.

•Visual regression testing keeps your software and tests working properly. This is the user interface (UI) tweak example mentioned earlier. Good self-healing eliminates a lot of redundant work, makes product teams more ambitious with A/B testing, and helps them respond quickly to trends.

•Data generation is useful along with major software testing tools. AI can be used to parameterize larger-scale tests, for example, generating large numbers of profile pictures with rare resolutions and metadata to see if users can upload them properly.

The best testing tool for artificial intelligence software testing

(1)Launchable

Launchable uses pattern recognition to see the likelihood of test failure. This information can be used to cut off the test suite and eliminate some obvious redundancies. Additionally, tests can be grouped, for example, to run only the most problematic tests before deploying a patch.

(2)Percy

Percy is a visual regression testing tool. It's great for keeping UI testing relevant and helps you maintain user interface consistency across different browsers and devices.

(3)mabl

mabl is a simple test automation platform with self-healing function. It preaches a low-code approach but works perfectly in the traditional way.

(4)Avo

Avo has a dedicated tool for managing test data, and this feature also includes artificial intelligence data generation. The solution claims to simulate real-world data at scale and do some data discovery on top.

Conclusion

The artificial intelligence approach in software testing is a truly powerful tool that improves efficiency even more than conventional automation. Some subsets may seem overkill (for example, data generation was before people started labeling everything "artificial intelligence"), but self-healing testing and pattern recognition are no small feats. As long as you set the right goals and find the right people, implementing AI into your quality assurance program is certainly worth it.

However, there is no point in introducing artificial intelligence into software testing without a good test management solution. A solid testing organization is required to dabble in AI, and any serious effort will have the added complexity of using multiple AI testing tools. Before embarking on your AI software testing journey, you need to make sure you find an ideal all-in-one test management solution.

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