Adaptive Bug Localization Framework for Precision-Driven Bug Localization in Software Engineering

Software development always looks for automated methods to improve productivity and accuracy in issue detection. The paper conducts a comparative examination of several machine-learning techniques to tackle the bug localization difficulty. Our study compared the performance of Logistic Regression (LR), Random Forest Classifier (RFC), Support Vector Machine (SVM), Gradient Boosting Classifier (GBC), and Adaptive Bug Localization System (ABLS) on five dataset versions. The results demonstrate the superior performance of ensemble learning methods. The ABLS model regularly beats other models regarding F1 score, accuracy, and recall, indicating its strong potential for precise problem localization. The study highlights the necessity of continuously adapting models to tackle idea drift in dynamic datasets. Our research suggests a path for future endeavours involving improving feature engineering and integrating real-time online learning to sustain high performance in bug localization activities.