Towards Effective Software Defect Prediction Using Machine Learning Techniques

Software defect prediction plays a crucial role in quality assurance by the early detection of possible flaws in the development process. Machine learning techniques have recently shown promising results, offering automated and accurate prediction models. This paper explores various machine learning techniques for software defect prediction, including supervised learning algorithms like logistic regression, naïve bayes, decision trees, and ensemble methods such as random forest. We delve into the process of feature selection, model training, and evaluation metrics commonly used in this context. Recent studies are reviewed, and challenges and future directions in software defect prediction using machine learning are highlighted. The research directions emphasize the integration of supervised machine learning techniques to detect defect while software development using ten promise repository datasets. By leveraging these techniques, software developers can boost the efficiency and effectiveness of defect detection, leading to improved overall software quality. This research underscores the importance of machine learning in developing robust defect prediction models and the continuous evolution of methodologies to tackle emerging challenges in the field.