Software Engineering (SE) research involving the use of Large Language Models (LLMs) has introduced several new challenges related to rigour in benchmarking, contamination, replicability, and sustainability. In this paper, we invite the research community to reflect on how these challenges are addressed in SE. Our results provide a structured overview of current LLM-based SE research at ICSE, highlighting both encouraging practices and persistent shortcomings. We conclude with recommendations to strengthen benchmarking rigour, improve replicability, and address the financial and environmental costs of LLM-based SE.
翻译:在涉及大型语言模型(LLMs)的软件工程(SE)研究中,基准测试的严谨性、数据污染、可复现性及可持续性等方面涌现出诸多新挑战。本文旨在引导研究界深入思考软件工程领域应对这些挑战的现状。我们的研究结果系统梳理了当前ICSE会议上基于LLM的软件工程研究,既揭示了值得推广的实践模式,也指出了长期存在的不足。最后,我们提出系列建议以增强基准测试的严谨性、提升研究可复现性,并应对基于LLM的软件工程研究产生的财务与环境成本。