Utilizing Machine Learning for Automated Software Testing

Abstract

Software testing is a critical phase in software development that ensures the reliability and quality of the final product. However, traditional manual testing methods are often time-consuming, error-prone, and unable to keep pace with the rapid development cycles of modern software. To address these challenges, researchers and practitioners have increasingly turned to automated testing techniques. Among these, machine learning (ML) holds promise for improving the efficiency and effectiveness of software testing processes. This paper provides an overview of the current state of utilizing machine learning for automated software testing, discussing key methodologies, challenges, and future directions in this evolving field.