Explainable AI-Based Bug Root Cause Analysis System for Intelligent Software Debugging

The increasing complexity of modern software systems has resulted in a rapid rise in software bugs and system failures. Debugging these issuesis often a time-consuming and laborintensive process for developers, particularly when dealing with large codebases and complex runtime environments. Traditional debugging approaches rely heavily on manual inspection of source code, log files, and stack traces, which may not always provide clear insights into the root cause of software failures. This research proposes an Explainable AI-Based Bug Root Cause Analysis System designed to assist developers in identifying and understanding software bugs more efficiently. The proposed system integrates machine learning,natural language processing, and explainable artificial intelligence techniques to analyze software logs, runtime data, and source code patterns. Machine learning models are used to detect potential bugs, while explainable AI techniques generate human-readable natural language processing, and explainable artificial intelligence techniques to analyze software logs, runtime data, and source code patterns. The system architecture includes data collection, preprocessing, bug detection, explainability analysis, and visualization modules. Experimental evaluation demonstrates that AI-based debugging systems can significantly reduce debugging time and improve software reliability by automatically identifying root causes of failures. The proposed approach enhances developer productivity and supports intelligent debugging through interpretable machine learning models.