Enhancing Software Testing with Machine Learning

Mouna Mothey

International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2023, vol. 9, issue 6, 407-413

Abstract: Software testing is essential for ensuring software quality and reliability but remains a resource-intensive process. Machine Learning (ML) holds promise for automating and optimizing testing activities, including test case generation, fault detection, and test prioritization. By leveraging predictive analytics and ML algorithms, testing becomes more effective, accurate, and adaptable. However, challenges such as the need for large, high-quality datasets and generalizability across software systems must be addressed. This report highlights ML's potential to revolutionize software testing while emphasizing the need for further empirical validation and careful model fine-tuning.

Keywords: Software Testing; Machine Learning; Test Automation; Fault Detection (search for similar items in EconPapers)
Date: 2023
Note: Article URL: https://ijsrcseit.com/CSEIT2390682
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Persistent link: https://EconPapers.repec.org/RePEc:jbh:ijsrcs:v9:y2023:i6:id:hcseit2390682

DOI: 10.32628/CSEIT2390682

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