Comparative Review on Automated Test Failure Detection and Healing Tools

The main aim of this paper was to evaluate automated test failure detection and healing tools in software test automation. Although Artificial Intelligence and Machine Learning involve creating separate and individual algorithms for accessing data and making sense of it by identifying patterns to form conclusions, these predictions should be used to their full benefit for software testing. Automated test failure detection and healing tools are one approach that makes more of these predictions become a reality under software testing. This paper reviews the existing literature regarding healing tools specifically created for test failure detection and healing, particularly their performance in recognizing User Interface changes and healing the test scripts automatically. The review presents the key characteristics, features, functionalities, and technologies used in the tools, such as Artificial Intelligence, machine learning, visual testing, and integration with popular test automation frameworks. By juxtaposing the sources reviewed above, the review outlines the pros and cons and promising application areas of each and provides suggestions for appropriate uses in highly diverse testing conditions and contexts. Moreover, the review also starts with the gaps and the challenges that the current cutting-edge approaches have faced and gives a future outlook on what directions future research and development have in terms of automated test failure detection and healing. Somewhere It seems like there is no distinctive technique framework or tool available that could support the automated test failure detection and healing and can fulfill all the requirements. Finally, this paper ends with a discussion of the most popular tools available, along with the expressed thought process about the present and forthcoming artificial intelligence for test automation.