What is AI-based Software Testing & Quality Assurance

Accurate testing results

Taking into account all abilities of artificial intelligence we can state with confidence that software testing executed by AI is more accurate than one executed by a human tester who is always exposed to stress, tiredness, overloads, boredom, and other factors that can lead to mistakes and negligence. AI-powered tools in these terms are more efficient as they can perform testing whenever needed with high accuracy.

Self-healed Selenium tests

Test flakiness is one of the major challenges of automation testing. AI can help to overcome it. For example, quite a few AI-driven tools for Selenium test self-healing have been brought to the market.

AI algorithms create appropriate locator strategies to let QA teams save time, get rid of maintenance hassles, and provide test stability. In case one of the locators is broken or missing the self-healing technology matches another most appropriate locator and heals a test on the fly during a test run, letting a tester know about the way the test has been modified.

Complete traceability

Integrated AI-driven analytics collects test outcomes and associates your tests with project requirements and code coverage to provide complete test traceability.

Efficient visual validation

The ability of AI to recognize images has found an active use in automated visual validation tools that check whether UI elements have the correct color, position, size, etc., and detect the bugs that usually are missed out on during manual testing.

Such tools also address the pain of ongoing changes in UI along with a constantly growing number of platforms and devices with different screen sizes, and configurations.

All in all, AI has made UI testing faster and more stable.

Selective testing

AI-powered tools are able to optimize software testing by executing only those tests that are required for checking code changes in a new build. In such a way there is no need to execute thousands of time-incentive tests but only the required ones to provide fast feedback.

Better test coverage

AI-powered automation testing tools execute many more tests of the most diverse types within a short time frame. In such a way QA teams can observe higher test coverage while product quality gets much better as well.

Test data generation

QA experts know that test data plays an important role in the effective software testing process. Therefore, it is important to take up time for such data creation/collection. Thanks to AI this process has become much easier as long as test data can be generated automatically on the basis of production data analysis. As a consequence, AI-generated test data is indeed similar to the real one which makes testing more accurate.

Reduced time to market

As long as AI considerably accelerates test execution, test maintenance, test data generation, and many other aspects of QA procedure, it contributes significantly to time to market reduction which is of high value for a business.

Applitools

Applitools is an AI-driven visual UI testing tool. It automatically checks the look and feel of applications and sites and smoothly integrates with existing tests. So there is no need to bother with the creation of new tests or learning a new test automation language. The entire screen of an application can be validated with a single code snippet. A check of visual perfection across all browsers and screens is just the work of a moment with Applitools.

Testim

Testim is an automated functional testing tool, utilizing AI and ML to accelerate test design, test execution, and test maintenance. In essence, Testim positions itself not as a tool, but rather as a platform because it also offers a test execution environment.

In terms of support, the tool can be run on various browsers and platforms.

Tests are drawn up either via a recorder, code, or both. The tests get stabilized and self-maintained over time as Testim utilizes historical data to rank locators for each UI element individually.

Testim also provides visual validations for pixels, fonts, texts, etc.

Test.AI

Test.ai serves as an Appium element finding plugin, allowing Appium users to use AI-based search strategies for the identification of UI elements. But the core goal of this AI-driven testing tool is to enable code-free test automation with an autonomous selection of elements and execution of tests. Testim uses special bots to inspect the functionality of an application under testing and perform regression testing with the capture of screenshots and performance metrics.

Mabl

Mabl focuses on regression testing. The embedded AI is trained by capturing user interactions and intents. With regard to these recordings, information on the previous test runs, and knowledge about other applications, the tool makes attempts to replicate a user input. Mabl also crawls for broken links, JS errors, visual modifications, and changes in time of page loading/test execution. The test accuracy is also improving with each test run since the tool learns how to differentiate in the best way dynamic and static regions for visual testing.

Eggplant AI

Eggplant AI is one more automation testing tool utilizing artificial intelligence and machine learning for auto-generation of tests, optimization of test execution, bug detection, and comprehensive test coverage.

To ensure a great end-user experience, Eggplant AI provides testing from the end-user perspective which includes app usability, functionality, and performance across the most diverse browsers, OSs, and devices.

The additional features of AI-driven testing provided by Eggplant AI are regression packs, bug hunting, and coverage analysis.