MACHINE LEARNING IN SOFTWARE TESTING: ENHANCING AUTOMATION AND ACCURACY

Abstract

Software testing is also well-known as an important part of software development practice, which guarantees reliability, functionality, and user satisfaction. But the old methods of testing that are highly manual and rely on rule-based automation have an issue of scalability, efficiency, and flexibility to quickly changing software environments. This paper examines the concept of machine learning (ML) integration into software testing, as a revolutionary process that will enable one to deal with these issues. Using mixed-methods analysis, based on case studies of open-source repositories and experimental analysis of ML models, including random forests, clustering, and neural networks, the results indicate considerable improvements in defects prediction accuracy, reduction of testing cost, and the minimization of human involvement. Results show that ML-based models can enhance defect prediction by 30% compared to the baseline methods, and regression tests optimization and automated test-case generation can be used to reduce execution costs by 25-40%. Besides, the potential of ML models to memorize sophisticated patterns on multidimensional datasets makes them an essential instrument in the acceleration of testing in agile and DevOp pipelines. However, the paper also notes severe constraints, such as explainability and cross-domain adaptability problems and the necessity of regular retraining to keep the accuracy. These results highlight the potential of an ML-based approach to testing as well as its limitations, which require the additional development of explainable AI, transfer learning, and governance frameworks. The study helps to move the discussion about intelligent testing forward and argues that ML should be seen as an agent of change in quality assurance in the digital age.