Reliability of LLM Inference Engines from a Static Perspective: Root Cause Analysis and Repair Suggestion via Natural Language Reports

Large Language Model (LLM) inference engines are becoming critical system infrastructure, yet their increasing architectural complexity makes defects difficult to be diagnosed and repaired. Existing reliability studies predominantly focus on model behavior or training frameworks, leaving inference engine bugs underexplored, especially in settings where execution-based debugging is impractical. We present a static, issue-centric approach for automated root cause analysis and repair suggestion generation for LLM inference engines. Based solely on issue reports and developer discussions, we construct a real-world defect dataset and annotate each issue with a semantic root cause category and affected system module. Leveraging text-based representations, our framework performs root cause classification and coarse-grained module localization without requiring code execution or specialized runtime environments. We further integrate structured repair patterns with a large language model to generate interpretable and actionable repair suggestions. Experiments on real-world issues concerning vLLMs demonstrate that our approach achieves effective root cause identification and module localization under limited and imbalanced data. A cross-engine evaluation further shows promising generalization to TensorRT-LLM. Human evaluation confirms that the generated repair suggestions are correct, useful, and clearly expressed. Our results indicate that static, issue-level analysis is a viable foundation for scalable debugging assistance in LLM inference engines. This work highlights the feasibility of static, issue-level defect analysis for complex LLM inference engines and explores automated debugging assistance techniques. The dataset and implementation will be publicly released to facilitate future research.