Machine Learning Algorithms for Automated Software Testing: Enhancing Efficiency and Reducing Errors
Keywords:
Software testing, Machine learning, Automated testing, Test case generation, Bug prediction, Regression testing, Fault detection, Test optimizationAbstract
Software testing is an integral part of software development, ensuring that software products meet required quality standards. Traditional testing techniques, while effective, are often time-consuming and error-prone due to human limitations. Machine learning (ML) algorithms have emerged as a promising solution for automating software testing, improving its efficiency, and reducing human-induced errors. This paper explores the application of ML algorithms in automating software testing, focusing on their role in enhancing test coverage, identifying defects, and optimizing test execution. We examine various ML models and techniques used in test case generation, bug prediction, fault detection, and regression testing, with case studies and examples illustrating their effectiveness in real-world scenarios. The results highlight the potential of ML to transform traditional testing processes and contribute to the development of high-quality, reliable software.
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