Enhancing Regression Testing Efficiency: A Machine Learning Approach to Test Case Prioritization

Abstract

Regression testing plays a pivotal role in maintaining software quality, especially in agile environments where frequent changes are made to the code. However, traditional regression testing methods are often inefficient due to their time-consuming nature and inability to effectively handle large-scale systems. To address these challenges, machine learning techniques offer a promising approach for optimizing test case prioritization, enabling more efficient and targeted testing. This paper explores various machine learning models, including supervised, unsupervised, and reinforcement learning, for test case prioritization in regression testing. It examines their impact on fault detection, execution time, and resource optimization. The paper also evaluates the strengths and weaknesses of each approach through real-world case studies, demonstrating the effectiveness of machine learning in enhancing testing efficiency. Furthermore, the study highlights the challenges industries face in adopting machine learning for regression testing, including issues related to data privacy, computational overhead, and the lack of skilled expertise. Overall, the application of machine learning in test case prioritization presents significant benefits, although practical barriers still need to be addressed for widespread adoption.