Enhancing Software Testing with Machine Learning

Abstract

Software testing is one of the most critical processes toward achieving software quality and reliability. However, this is a time-consuming and resource-intensive process. Integration of Machine Learning into such a process in software testing could be seen as promising for automating or optimising such processes. This report discusses how ML techniques can assist in streamlining some of these testing activities, such as test case generation, fault detection, and test prioritization. Predictive analytics and ML algorithms make testing better in terms of effectiveness, accuracy, and adaptability. Although much has been accomplished, there are many issues related to fully implementing ML in traditional testing frameworks that still need research.