Improving effort-aware defect prediction using machine learning methods

Software development greatly benefits from defect prediction, which will prioritize testing modules with the highest potential for defects. Traditional effort-aware defect prediction (EADP) methods usually contain regression or classification methods, which might not fully represent the complexity of software features. To improve feature selection and ranking, this study offers an improved EADP model that uses modern machine learning algorithms such as XGBoost, AdaBoost, and Random Forest. Also, the demonstrated study integrates a synergy-based fea- ture fusion and attention mechanism to improve the prioritization of defective modules. The suggested method improves prediction efficiency and accuracy by directly adjusting the Proportion of Found Bugs at 20% of LOC (PofB@20%). The findings from experiments gathered across different public datasets show that the proposed model improves current methods with considerable improve- ments in PofB at 20% and decreases in initial false alarms (IFA). These findings show that combining gradient-boosting approaches with synergy-based fusion and attention-weighting tricks can significantly improve EADP models, which has significant benefits for software testing and quality control.