AN EXPLAINABLE DEEP LEARNING FRAMEWORK FOR PREDICTIVE SOFTWARE QUALITY ENGINEERING AND AUTOMATED DEFECT DETECTION

Abstract

Software Quality Engineering (SQE) is evolving from reactive defect detection toward predictive and intelligent quality assurance. Traditional testing approaches identify defects after implementation and require extensive manual effort to analyze source code, test results, historical defects, and execution behavior. Machine learning and deep learning techniques enable prediction of defect-prone software components before failures occur; however, many deep learning models operate as black boxes, making their predictions difficult for developers, testers, and quality engineers to understand and validate. This paper proposes the Explainable Deep Learning Framework for Predictive Software Quality Engineering and Automated Defect Detection (XDL-SQE). The framework integrates static code metrics, change history, historical defect data, test execution results, runtime information, and source-code embeddings. A hybrid deep learning engine (CNN-LSTM-Transformer) generates defect-risk predictions, while an explainability layer based on SHAP and LIME provides feature attribution. A quality-prioritization layer converts predictions into actionable testing recommendations integrated with CI/CD pipelines. The framework addresses five key gaps: prediction without explanation, fragmented quality data, limited testing integration, human trust, and cross-project generalization. A comprehensive research protocol is proposed for evaluating predictive accuracy, explanation quality, testing efficiency, and operational usefulness. This work contributes a unified architecture for interpretable, actionable, and continuous predictive quality engineering.