Enhanced Bug Detection in Compiler Logs Using Stacking and Explainable AI Techniques

The software development process indeed comes up with compiler related bugs, it is critical to identify and fix them correctly to improve software quality. Compiler bugs are very critical to the correctness of software applications; thus, their detection and fixes are important. This paper proposes a new method that uses ensemble learning and explainable AI to improve the identification of compiler bugs. This paper focuses on the ensemble of four classifiers: Random Forest, Support Vector Machine, Neural Network, and Gradient Boosting. Incorporating these models should enhance the predictive accuracy and generalization ability since the individual classifiers have their drawbacks. In order to make the model more interpretable, we use SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations) to explain which features affect bug detection decisions. The results reveal the superior performance of the proposed ensemble model which yields better results than each of the individual classifiers in terms of precision, recall, F1-score, and ROC-AUC. In addition to enhancing the bug detection rate with the help of state-of-the-art machine learning and explainable AI, the method even enhances the understanding of compiler log analysis and significantly contributes to the field of software quality assurance.