Towards Evaluation Guidelines for Empirical Studies involving LLMs
In the short period since the release of ChatGPT in November 2022, large language models (LLMs) have changed the software engineering research landscape. While there are numerous opportunities to use LLMs for supporting research or software engineering tasks, solid science needs rigorous empirical evaluations. However, so far, there are no specific guidelines for conducting and assessing studies involving LLMs in software engineering research. Our focus is on empirical studies that either use LLMs as part of the research process (e.g., for data annotation) or studies that evaluate existing or new tools that are based on LLMs. This paper contributes the first set of guidelines for such studies. Our goal is to start a discussion in the software engineering research community to reach a common understanding of what our community standards are for high-quality empirical studies involving LLMs.
Sat 3 MayDisplayed time zone: Eastern Time (US & Canada) change
09:00 - 10:30 | |||
09:00 15mOther | Welcome WSESE | ||
09:15 50mKeynote | The Methodological Implications of Using Generative AI in Software Engineering Research WSESE Margaret-Anne Storey University of Victoria | ||
10:05 12mTalk | Towards Evaluation Guidelines for Empirical Studies involving LLMs WSESE Stefan Wagner Technical University of Munich, Marvin Muñoz Barón Technical University of Munich, Falessi Davide University of Rome Tor Vergata, Sebastian Baltes University of Bayreuth | ||
10:17 13mLive Q&A | Keynote & ESE4ML: Discussion WSESE |