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<p>Large Language Models have significantly advanced software engineering, enabling tasks like code comprehension and fault detection. However, their ability to detect complex bugs, such as data races in parallel programming, remains uncertain. Fault detection in parallel programming (Pthreads) requires a deep understanding of thread-based logic, as data races occur when threads access shared data concurrently without proper synchronization. This paper explores ChatGPT’s potential in Pthreads fault detection by addressing three questions: <b>(1) Can ChatGPT effectively debug parallel programming threads? (2) How can dialogue assist with the detection of faults? (3) How can prompt engineering help to improve ChatGPT’s fault detection performance?</b>. We examine advanced prompt engineering techniques, such as Zero-Shot, Few-Shot, Chain-of-Thought, and Retrieval-Augmented Generation prompts. Additionally, we introduce three hybrid prompting techniques to enhance performance, including Chain-of-Thought with Few-Shot Prompting, Retrieval-Augmented Generation with Few-Shot Prompting, and Prompt Chaining with Few-Shot Prompting, while evaluating ChatGPT’s strengths and limitations for data race detection.</p>

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Evaluating ChatGPT’s strengths and limitations for data race detection in parallel programming via prompt engineering

  • 期刊论文

  • 发表日期：

    2025年4月23日

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  • May Alsofyani,
  • Liqiang Wang

摘要

Large Language Models have significantly advanced software engineering, enabling tasks like code comprehension and fault detection. However, their ability to detect complex bugs, such as data races in parallel programming, remains uncertain. Fault detection in parallel programming (Pthreads) requires a deep understanding of thread-based logic, as data races occur when threads access shared data concurrently without proper synchronization. This paper explores ChatGPT’s potential in Pthreads fault detection by addressing three questions: (1) Can ChatGPT effectively debug parallel programming threads? (2) How can dialogue assist with the detection of faults? (3) How can prompt engineering help to improve ChatGPT’s fault detection performance?. We examine advanced prompt engineering techniques, such as Zero-Shot, Few-Shot, Chain-of-Thought, and Retrieval-Augmented Generation prompts. Additionally, we introduce three hybrid prompting techniques to enhance performance, including Chain-of-Thought with Few-Shot Prompting, Retrieval-Augmented Generation with Few-Shot Prompting, and Prompt Chaining with Few-Shot Prompting, while evaluating ChatGPT’s strengths and limitations for data race detection.