THE SYSTEMATIC REVIEW STUDY OF SIGNIFICANCE OF MACHINE LEARNING TECHNIQUES IN SOFTWARE DEFECT PREDICTION
Keywords:
Software Defects, Machine Learning, Prediction, ReviewAbstract
Software defects remain a critical challenge in the software development lifecycle, impacting product quality, cost, and delivery time. Predicting and mitigating defects early in the development process is crucial to ensure successful software projects. This research paper provides a comprehensive review and analysis of software defect prediction techniques using machine learning. We examined various machine learning algorithms, feature selection methods, and datasets commonly used in this domain. Furthermore, we present a comparative evaluation of these approaches, highlighting their strengths and weaknesses, and discuss future research directions to improve the accuracy and effectiveness of defect prediction models.
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