The global AI market is expected to reach $60 billion by 2025. Artificial Intelligence has the ability to mimic human intelligence, so it’s easy to see why people assume that it should apply to laborious IT tasks. The hope is that AI can reduce test failures, provide quicker feedback, and increase test reliability by extracting patterns from data to make decisions and speeding up test runs.
One aspect holding back the uptake of AI in software testing is the uncertainty surrounding how accurate the tools will be when compared to what human testers can do. The only way we will achieve this is through lots of data, time, and effort to make the AI intelligent enough to test software reliably. In many cases, it’s tough to imagine how human software testers will teach the AI to know if a code is correct or not. In some cases, the challenge could be impossible to overcome.
While AI won’t replace human testers anytime soon, there is a lot of opportunity for human testers and developers to start teaching AI to be reliable and effective. We’re looking forward to seeing how developers and testers continue to identify different testing situations where AI can be used accurately and efficiently alongside the people who are tasked with this critical part of the software development lifecycle.
Application versions are compared in each build and differences are classified. Launchable is a differential testing tool that is based on a machine learning algorithm that predicts the likelihood of failure for each test. Google OSS-Fuzz is another tool that supports C/C++, Rust, Go, and Python.
Image-based learning and screen comparisons are done to test the look and feel of an app. Applitools integrates with many tools (such as Selenium and Appium), and it helps with cross-browser and cross-device testing and can speed up your functional and visual testing by 30 times. Percy by BrowserStack is another tool that can be used to automate visual testing.
The goal here is to specify the intent of the test and then the system decides how the test should be performed. Tricentis combines test data design and generation, test case design, test automation, and analytics to test GUIs and APIs.
When the UI changes, the element selection in tests gets auto-corrected. Mabl uses machine learning algorithms to improve defect detection and improve test execution. Testim uses AI and machine learning to speed up authoring, execution, automation, and maintenance of tests.