A Literature Review on Software Defect Prediction: Trends, Methods, and Frameworks

Abstract

Identifying possible problems at an early point in the development lifecycle is one of the most important things that software defect prediction can do to enhance software quality and minimize development costs. This is one of the most crucial roles that software defect prediction can play. Of all the functions that software can perform, this is one of the most crucial ones. This literature review aims to offer a thorough examination of the research trends, methodologies, and frameworks utilized in the field of software defect prediction. This study analyzes a broad range of scholarly publications. These publications cover a wide variety of topics related to defect prediction, including dataset features, prediction models, assessment measures, and prediction approaches. Within the context of minimizing the negative consequences of defects on software quality and project schedules, the review emphasizes the significance of software defect prediction. This investigation identifies significant research themes such as the use of machine learning algorithms, feature selection approaches, and ensemble methods in defect prediction. The paper also scrutinizes the challenges and limitations associated with the diverse defect prediction methodologies currently in use. These include the imbalance of the dataset, the bias in feature selection, and the overfitting of the model. Additionally, it highlights the development of research fields and the opportunities for future study, such as the incorporation of domain knowledge, the incorporation of varied data sources, and the development of advanced approaches to predictive modeling. Furthermore, it acknowledges the existence of these opportunities. In its entirety, this literature review provides researchers and practitioners working in the field of software engineering with critical insights into the present state of the art in software defect prediction.