一句话：利用零样本大语言模型自动识别开发者交流中的情绪及其成因，以促进开源协作。
理解开发者情绪背后的原因（例如合并拉取请求延迟导致的沮丧）对于解决问题和促进开源社区协作至关重要。在聊天、邮件和 issue 评论等大量项目沟通中有效识别此类信息，需要自动识别情绪及其原因。实现这种自动化通常需要大规模、软件工程专用的数据集来训练准确的机器学习模型；然而，由于软件项目沟通渠道的多样性和非正式性，构建此类数据集成本高昂。本文采用零样本大语言模型（zero-shot LLM）来揭示开发者交流中的情绪原因，以绕开对大规模标注数据的依赖。摘要未报告具体评估结果或性能指标。
Understanding and identifying the causes behind developers' emotions (e.g., Frustration caused by 'delays in merging pull requests') can be crucial towards finding solutions to problems and fostering collaboration in open-source communities. Effectively identifying such information in the high volume of communications across the different project channels, such as chats, emails, and issue comments, requires automated recognition of emotions and their causes. To enable this automation, large-scale software engineering-specific datasets that can be used to train accurate machine learning models are required. However, such datasets are expensive to create with the variety and informal nature of software projects' communication channels.
@inproceedings{paperbot11289,
title = {Uncovering the Causes of Emotions in Software Developer Communication Using Zero-shot LLMs},
author = {Mia Mohammad Imran and Preetha Chatterjee and Kostadin Damevski},
booktitle = {ICSE},
year = {2024},
doi = {10.1145/3597503.3639223}
}