Leveraging machine learning for intelligent test automation: Enhancing efficiency and accuracy in software testing

This paper discusses the possibility of applying machine learning (ML) and automation in the software testing process to improve the quality assurance process. The problem is that as software systems increase in size and functionality, more than traditional test approaches may be required, they become slow and error-prone. AI and ML can be used in software testing to reduce the manual approach and increase testing efficiency by improving testing quality. The work focuses on several AI-based approaches, including automated test case generation, intelligent test case prioritization, anomaly detection, and defect prediction. Real-life examples and studies show the effectiveness of these techniques in decreasing the time spent on manual testing, increasing the test precision, and increasing the system reliability. Nonetheless, the issues still open include the practicability of the proposed ML models, the training time, data sets, and the applicability of the entire framework across other software environments. This study shows how the testing process can be transformed with the help of AI-based testing, what issues may arise, and how further research can be conducted.