Machine Learning Applications in Software Defect Prediction

Abstract

Software defect prediction is an essential activity in ensuring the reliability and quality of software systems. This paper explores the application of machine learning (ML) techniques in predicting software defects at various stages of the software development lifecycle. We survey popular ML models including decision trees, support vector machines, neural networks, and ensemble methods that have been employed to analyze software metrics, historical defect data, and code complexity measures. The paper highlights key datasets such as NASA’s Metrics Data Program and PROMISE repository that have been extensively used in defect prediction research. Experimental comparisons are provided, demonstrating the relative strengths and weaknesses of various algorithms in terms of accuracy, precision, recall, and computational efficiency. The study also addresses challenges such as imbalanced datasets, feature selection, and model interpretability. The potential for integrating ML-based defect prediction systems into continuous integration pipelines is explored to enable proactive defect management.

KEYWORDS: Machine Learning, Software Defect Prediction, Code Metrics, Ensemble Methods, Continuous Integration