Breaking the Silence: the Threats of Using LLMs in Software Engineering
- URL: http://arxiv.org/abs/2312.08055v2
- Date: Mon, 8 Jan 2024 14:30:14 GMT
- Title: Breaking the Silence: the Threats of Using LLMs in Software Engineering
- Authors: June Sallou, Thomas Durieux, Annibale Panichella
- Abstract summary: Large Language Models (LLMs) have gained considerable traction within the Software Engineering (SE) community.
This paper initiates an open discussion on potential threats to the validity of LLM-based research.
- Score: 12.368546216271382
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Large Language Models (LLMs) have gained considerable traction within the
Software Engineering (SE) community, impacting various SE tasks from code
completion to test generation, from program repair to code summarization.
Despite their promise, researchers must still be careful as numerous intricate
factors can influence the outcomes of experiments involving LLMs. This paper
initiates an open discussion on potential threats to the validity of LLM-based
research including issues such as closed-source models, possible data leakage
between LLM training data and research evaluation, and the reproducibility of
LLM-based findings. In response, this paper proposes a set of guidelines
tailored for SE researchers and Language Model (LM) providers to mitigate these
concerns. The implications of the guidelines are illustrated using existing
good practices followed by LLM providers and a practical example for SE
researchers in the context of test case generation.
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