A Review of AI-augmented End-to-End Test Automation Tools

Abstract

Software testing is a process of evaluating and verifying whether a software product still works as expected, and it is repetitive, laborious, and time-consuming. To address this problem, automation tools have been developed to automate testing activities and enhance quality and delivery time. However, automation tools become less effective with continuous integration and continuous delivery (CI/CD) pipelines when the system under test is constantly changing. Recent advances in artificial intelligence and machine learning (AI/ML) present the potential for addressing important challenges in test automation. AI/ML can be applied to automate various testing activities such as detecting bugs and errors, maintaining existing test cases, or generating new test cases much faster than humans. In this study, we will outline testing activities where AI has significantly impacted and greatly enhanced the testing process. Based on that, we identify primary AI techniques that are used in each testing activity. Further, we conduct a comprehensive study of test automation tools to provide a clear look at the role of AI/ML technology in industrial testing tools. The results of this paper help researchers and practitioners understand the current state of AI/ML applied to software testing, which is the first important step towards achieving successful and efficient software testing.