LLM-Based Identification of Null Pointer Exception Patches

Null Pointer Exceptions (NPEs) are one of the leading causes of software crashes and runtime errors. Although existing methods attempt to detect and classify NPE fixes, they often fall short due to irrelevant or noisy data, a lack of contextual understanding, and inefficiency in processing large and imbalanced datasets. To overcome these challenges, we propose an approach, called Augmented Agentic Commit Classification (AACC for short), to accurately categorize commit patches as NPE fixes or non-NPE. AACC leverages the code structure and contextual insights from commit messages to capture the semantic intent behind code modifications. It features four key advancements: (1) Best example selection that filters high-quality, contextually relevant commits to ensure the model learns from contextual rich and accurate data; (2) an augmented knowledge base that enriches classification by combining contextual metadata, program semantics, and bug fix patterns; (3) a prioritise agent that ranks commits based on relevance and impact, optimizing resource allocation and boosting efficiency; and (4) an iterative refinement process that enables the model to learn from feedback to correct misclassifications, reducing false negative rates. Our evaluation results on ChatGPT-4o suggest that it outperforms the state-of-the-art approaches by improving the F1 score from 72.07% to 98.03%.