Data Race Detection Using Large Language Models

• URL: http://arxiv.org/abs/2308.07505v2
• Date: Tue, 3 Oct 2023 06:09:18 GMT
• Title: Data Race Detection Using Large Language Models
• Authors: Le Chen, Xianzhong Ding, Murali Emani, Tristan Vanderbruggen, Pei-hung Lin, Chuanhua Liao
• Abstract summary: Large language models (LLMs) are an alternative strategy to facilitate analyses and optimizations of high-performance computing programs. In this paper, we explore a novel LLM-based data race detection approach combining prompting engineering and fine-tuning techniques.
• Score: 1.0013600887991827
• License: http://creativecommons.org/licenses/by/4.0/
• 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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