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Enhancing Software Testing with Machine Learning

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  • Mouna Mothey

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.

Suggested Citation

  • Mouna Mothey, 2023. "Enhancing Software Testing with Machine Learning," International Journal of Scientific Research in Computer Science, Engineering and Information Technology, International Journal of Scientific Research in Computer Science, Engineering and Information Technology, vol. 9(6), pages 407-413, November.
    • Handle: RePEc:jbh:ijsrcs:v9:y2023:i6:id:hcseit2390682
    DOI: 10.32628/CSEIT2390682
    Note: Article URL: https://ijsrcseit.com/CSEIT2390682
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