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Resource leaks, caused by resources not being released after acquisition,often lead to performance issues and system crashes. Existing static detectiontechniques rely on mechanical matching of predefined resourceacquisition/release APIs, posing challenges to their effectiveness, includingcompleteness of predefined APIs, identification of reachability validation, andanalysis complexity. To overcome these challenges, we propose InferROI, a novelapproach that leverages large language models (LLMs) to directly inferresource-oriented intentions (acquisition, release, and reachabilityvalidation) in code, based on resource management knowledge and code contextunderstanding, rather than mechanical API matching. InferROI uses a prompt toinstruct the LLM in inferring involved intentions from a given code snippet,which are then translated into formal expressions. By aggregating theseinferred intentions, InferROI utilizes a lightweight static-analysis basedalgorithm to analyze control-flow paths extracted from the code, therebydetecting resource leaks.  We evaluate InferROI on Java program and investigate its effectiveness inboth resource-oriented intention inference and resource leak detection.Experimental results demonstrate that InferROI achieves a precision of 74.6%and a recall of 81.8% in intention inference on 172 code snippets from theDroidLeaks dataset. Additionally, InferROI covers a significant portion ofconcerned Android resources listed in the dataset. When applied to 86 bugs fromthe DroidLeaks dataset, InferROI exhibits a high bug detection rate (53.5%) anda low false alarm rate (8.1%) compared to eight baseline detectors. Moreover,we apply InferROI to resource leak detection in 100 methods from real-worldopen-source projects, where it identifies 12 unknown resource leak bugs, with 7of them being confirmed by developers.

LLM-based Resource-Oriented Intention Inference for Static Resource Leak Detection