Machine Learning Approaches for Software Defect Prediction

Abstract

This paper analyses existing research about machine learning approaches in software defect prediction as a key element for improving software reliability and quality. The paper reviews the use of machine learning algorithms in software defect prediction framework’s bug prediction while assessing their performance across multiple environments. A comprehensive review of scholarly literature enables researchers to specify both advantages and drawbacks that emerge when using machine learning for automated defect detection in software defect prediction applications. The review conducts assessments of typical metrics like accuracy and precision and recall and runtime performance yet extends its evaluation to analyze new trends combining deep learning with ensemble approaches to enhance software defect prediction functionality. The examined findings provide crucial guidelines which help developers select and improve machine learning models in software defect prediction processes that result in better software reliability and robustness.