An Intelligent Machine Learning Framework for Early Software Defect Prediction in Large-Scale Software Systems
Keywords:
software defect prediction, machine learning, ensemble learning, class imbalance, code metrics, software quality, feature selectionAbstract
Software defects discovered late in the development lifecycle are costly to fix and threatenreliability, motivating predictive approaches that flag defect-prone modules early. This studyproposes an intelligent machine-learning framework for early software defect prediction in large-scale systems, integrating rigorous feature engineering, class-imbalance handling
References
[1] Boehm, B., & Basili, V. R. (2001). Software defect reduction top 10 list. Computer, 34(1), 135–137. https://doi.org/10.1109/2.962984
[2] Menzies, T., Greenwald, J., & Frank, A. (2007). Data mining static code attributes to learn defect predictors. IEEE Transactions on Software Engineering, 33(1), 2–13. https://doi.org/10.1109/TSE.2007.256941
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Published
2022-11-15
How to Cite
Chandra Mohan Ravuri. (2022). An Intelligent Machine Learning Framework for Early Software Defect Prediction in Large-Scale Software Systems. Journal of Computational Analysis and Applications (JoCAAA), 30(2), 1316–1328. Retrieved from https://www.eudoxuspress.com/index.php/pub/article/view/5864
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