Prompts Matter: Insights and Strategies for Prompt Engineering in Automated Software Traceability
Prompts Matter: Insights and Strategies for Prompt Engineering in Automated Software Traceability
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DOI：
10.1109/rew57809.2023.00087
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发表时间：
2023-08
期刊：
影响因子：
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通讯作者：
Alberto D. Rodriguez;Katherine R. Dearstyne;J. Cleland-Huang
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文献类型：
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作者：
Alberto D. Rodriguez;Katherine R. Dearstyne;J. Cleland-Huang
大型语言模型（LLMS）有可能克服以前方法面临的挑战并引入新的可能性，从而彻底改变自动可追溯性。但是，LLM对自动可追溯性的最佳利用尚不清楚。本文探讨了及时工程的过程，从LLM提取链接预测。我们为构建有效提示的方法提供了详细的见解，为我们的课程提供了教训。此外，我们提出了多种策略来利用LLMS生成可追溯性链接，从而改善了先前的零击方法，以迅速改进后候选链接的排名。本文的主要目的是通过强调建造可追溯性的过程来激发未来的研究人员和工程师，这会有效利用LLM，以提高自动可追溯性。
Large Language Models (LLMs) have the potential to revolutionize automated traceability by overcoming the challenges faced by previous methods and introducing new possibilities. However, the optimal utilization of LLMs for automated traceability remains unclear. This paper explores the process of prompt engineering to extract link predictions from an LLM. We provide detailed insights into our approach for constructing effective prompts, offering our lessons learned. Additionally, we propose multiple strategies for leveraging LLMs to generate traceability links, improving upon previous zero-shot methods on the ranking of candidate links after prompt refinement. The primary objective of this paper is to inspire and assist future researchers and engineers by highlighting the process of constructing traceability prompts to effectively harness LLMs for advancing automatic traceability.