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arXiv · 2308.07505

Data Race Detection Using Large Language Models

Le Chen·Xianzhong Ding·Murali Emani·Tristan Vanderbruggen·Pei-hung Lin·Chuanhua Liao
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Abstract

Large language models (LLMs) are demonstrating significant promise as an alternate strategy to facilitate analyses and optimizations of high-performance computing programs, circumventing the need for resource-intensive manual tool creation. In this paper, we explore a novel LLM-based data race detection approach combining prompting engineering and fine-tuning techniques. We create a dedicated dataset named DRB-ML, which is derived from DataRaceBench, with fine-grain labels showing the presence of data race pairs and their associated variables, line numbers, and read/write information. DRB-ML is then used to evaluate representative LLMs and fine-tune open-source ones. Our experiment shows that LLMs can be a viable approach to data race detection. However, they still cannot compete with traditional data race detection tools when we need detailed information about variable pairs causing data races.

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Le Chen, Xianzhong Ding, Murali Emani, Tristan Vanderbruggen, Pei-hung Lin, Chuanhua Liao. 2023-08-15. Data Race Detection Using Large Language Models. https://doi.org/10.1145/3624062.3624088

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