Explainable AI for Detecting and Explaining Software Bugs in Java Applications

Software bugs can cause incorrect results, crashes, security weaknesses, and expensive maintenance work. Traditionaltesting and static-analysis tools can identify many defects, but their warnings may be difficult to prioritise or understand,especially in large Java applications. Artificial intelligence (AI) can help rank suspicious code and recognise patternsassociated with defects; however, a prediction is less useful when developers cannot understand why it was produced. Thispaper proposes an explainable AI (XAI) workflow for detecting and explaining potential bugs in Java applications. Theproposed approach combines source-code features, static-analysis findings, test and version-history signals, and aprediction component with explanations tied to observable evidence such as a risky code path, a missing null check, aresource-handling pattern, or a related test failure. The design aims to present a confidence score, relevant code location,supporting signals, and suggested next steps while keeping the developer responsible for confirming the defect. The paperdiscusses the problem, related approaches, architecture, methodology, evaluation plan, applications, limitations, and futurework. Since this is a proposed design, performance improvements must be established through experiments rather thanassumed