Design And Analysis of Software Defect Prediction Model Using Machine Learning Process

Abstract

The software defect prediction (SDP) in the ever-evolving field of software engineering. newlineWith increasing complexity in software systems, early defect detection is vital to reduce newlinedevelopment costs, enhance reliability, and ensure customer satisfaction. The section identifies newlinekey challenges, including the imbalanced nature of defect datasets, the selection of relevant newlinefeatures, and the scalability of prediction models. The research objectives are clearly defined, newlinefocusing on designing a robust prediction model leveraging ensemble learning techniques and newlineaddressing class imbalance using the Synthetic Minority Oversampling Technique (SMOTE). newlineThis SECTION sets the stage for methodological and experimental exploration in subsequent newlineparts. newlineCritically reviews prior research on software defect prediction, categorizing them into newlinesingle-model approaches (e.g., decision trees, SVM) and ensemble-based methods (e.g., Random newlineForest, AdaBoost). It highlights the evolution of techniques for handling class imbalance, newlineincluding oversampling, under sampling, and SMOTE. The review underscores the limitations of newlinetraditional methods, such as overfitting, poor generalization across projects, and the inability to newlinehandle real-world imbalanced datasets effectively. Furthermore, gaps in the current literature are newlineidentified, such as the lack of comprehensive evaluations across multiple datasets and the newlineunderutilization of advanced ensemble techniques. These gaps provide a foundation for the novel newlinecontributions of this research. newlineResearch Methodology elaborates on the methodology for developing an efficient and newlineaccurate software defect prediction model. A hybrid ensemble framework is proposed, newlinecombining Random Forest, AdaBoost, and Bagging to enhance predictive accuracy and newlinerobustness.