ENHANCING SOFTWARE QUALITY ASSURANCE THROUGH MACHINE LEARNING: TECHNIQUES, APPLICATIONS, AND CASE STUDIES

Abstract

An overview of the relationship between machine learning (ML) and software quality assurance (SQA) is provided in this abstract, with particular attention paid to case studies, techniques, and applications. One potential solution to the difficulties and complexities of contemporary software development processes is the incorporation of machine learning into software quality assurance (SQA). This abstract explores a number of machines learning approaches, including supervised learning, unsupervised learning, and deep learning, and shows how they may be applied to improve test case creation, defect prediction, anomaly detection, and automated testing, among other SQA functions. It also covers the various uses of ML in SQA in a variety of industries, including e-commerce, banking, and healthcare. This abstract explains real-world applications of ML-driven SQA approaches through a compilation of pertinent case studies, emphasizing their efficacy in enhancing software reliability, decreasing testing efforts, and speeding time-to-market. All things considered, this abstract offers a thorough manual for scholars, practitioners, and business experts who are curious about how machine learning might advance software quality assurance procedures.