What is AI-based Software Testing & Quality Assurance

Accurate testing results

Considering all the capabilities of artificial intelligence, we can confidently state that software testing executed by AI is more accurate than that executed by a human tester, who is always susceptible 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, but AI can help to overcome it. For example, quite a few AI-driven tools for Selenium test self-healing have been introduced to the market.

AI algorithms create appropriate locator strategies to save QA teams time, eliminate maintenance hassles, and ensure test stability. If one of the locators is broken or missing, the self-healing technology finds the most appropriate alternative locator and heals the test on the fly during a test run, informing the tester about how the test has been modified.

Complete traceability

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

Efficient visual validation

The ability of AI to recognize images has been actively utilized in automated visual validation tools that verify whether UI elements have the correct color, position, size, etc., and detect bugs that are usually missed during manual testing.

These tools also tackle the challenge of ongoing changes in UI, along with the constantly growing number of platforms and devices with different screen sizes and configurations.

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

Selective testing

AI-powered tools can optimize software testing by executing only those tests that are required to check code changes in a new build. In this way, there is no need to execute thousands of time-intensive tests, but only the necessary ones, to provide fast feedback.

Better test coverage

AI-powered automation testing tools can execute many more tests of the most diverse types within a short timeframe. In this way, QA teams can achieve higher test coverage, and product quality significantly improves as well.

Test data generation

QA experts understand that test data plays a crucial role in the effective software testing process. Therefore, dedicating time to data creation/collection is important. Thanks to AI, this process has become much easier, as test data can now be generated automatically based on the analysis of production data. Consequently, AI-generated test data closely resembles real data, which makes testing more accurate.

Reduced time to market

As AI significantly accelerates test execution, test maintenance, test data generation, and many other aspects of the QA process, it contributes substantially to reducing time to market, which is of high value for businesses.

Applitools

Applitools offers an AI-driven visual UI testing tool designed to automatically assess the appearance of applications and websites. It integrates seamlessly with existing tests, eliminating the need for creating new tests or learning a new test automation language. With Applitools, validating the entire screen of an application is possible with just a single code snippet, allowing for quick checks of visual consistency across all browsers and screens.

Testim

Testim provides an automated functional testing platform that leverages AI and ML to enhance test design, execution, and maintenance. It distinguishes itself not merely as a tool but as a comprehensive platform, including a test execution environment. Testim supports various browsers and platforms, offering test creation through a recorder, code, or a combination of both. It uses historical data to improve locator accuracy for UI elements and offers visual validations for elements like pixels, fonts, and text.

Test.AI

Test.AI functions as an Appium element finding plugin, utilizing AI-based search strategies for identifying UI elements. Its primary aim is to facilitate code-free test automation, enabling autonomous element selection and test execution. Test.AI employs specialized bots to assess application functionality during testing and conducts regression testing, including screenshot captures and performance metric analysis.

Mabl

Mabl specializes in regression testing, utilizing AI trained on user interactions and intents to replicate user input accurately. It also performs checks for broken links, JavaScript errors, visual changes, and variations in page loading/test execution times. Mabl's test accuracy improves with each run, as it learns to distinguish more effectively between dynamic and static regions for visual testing.

Eggplant AI

Eggplant AI is an automation testing tool that uses artificial intelligence and machine learning to auto-generate tests, optimize test execution, identify bugs, and achieve comprehensive test coverage. It focuses on delivering a superior end-user experience by testing from the user's perspective, covering app usability, functionality, and performance across a wide range of browsers, operating systems, and devices. Eggplant AI also features regression packs, bug hunting, and coverage analysis.