Boosting Static Resource Leak Detection via LLM-based Resource-Oriented Intention Inference

TL;DR

This paper introduces InferROI, a novel approach that uses large language models to improve static resource leak detection by inferring resource intentions directly from code, leading to higher bug detection rates.

Contribution

InferROI leverages LLMs for resource intention inference combined with static analysis, addressing limitations of traditional methods and enhancing leak detection accuracy.

Findings

Achieves 59.3% and 62.5% bug detection rates on datasets

Detects 14-45 and 149-485 more bugs than existing tools

Identifies 29 new resource leak bugs in real-world projects

Abstract

Resource leaks, caused by resources not being released after acquisition, often lead to performance issues and system crashes. Existing static detection techniques rely on mechanical matching of predefined resource acquisition/release APIs and null-checking conditions to find unreleased resources, suffering from both (1) false negatives caused by the incompleteness of predefined resource acquisition/release APIs and (2) false positives caused by the incompleteness of resource reachability validation identification. To overcome these challenges, we propose InferROI, a novel approach that leverages the exceptional code comprehension capability of large language models (LLMs) to directly infer resource-oriented intentions (acquisition, release, and reachability validation) in code. InferROI first prompts the LLM to infer involved intentions for a given code snippet, and then incorporates a two-stage static analysis approach to check control-flow paths for resource leak detection based on the inferred intentions. We evaluate the effectiveness of InferROI in both resource-oriented intention inference and resource leak detection. Experimental results on the DroidLeaks and JLeaks datasets demonstrate InferROI achieves promising bug detection rate (59.3% and 62.5%) and false alarm rate (18.6% and 19.5%). Compared to three industrial static detectors, InferROI detects 14~45 and 149~485 more bugs in DroidLeaks and JLeaks, respectively. When applied to real-world open-source projects, InferROI identifies 29 unknown resource leak bugs (verified by authors), with 7 of them being confirmed by developers. In addition, the results of an ablation study underscores the importance of combining LLM-based inference with static analysis.