一句话：提出开源工具 csDetector，用机器学习自动检测软件开发社区中的社区气味并提供社会技术度量。
社区气味是软件开发社区中组织与社会结构次优问题的症状，往往导致项目成本增加和软件质量下降。为自动检测这些气味并理解其组织-社会结构特征，本文提出开源工具 csDetector。该工具采用基于机器学习的检测方法，从现有不良社区开发实践中学习，自动识别项目中的社区气味并给出相关社会技术度量。作者在来自 GitHub 的 143 个开源项目基准上评估其有效性，结果表明 csDetector 能检测十种常见社区气味，平均 F1 分数为 84%。工具已公开并提供演示视频。
Community smells represent symptoms of sub-optimal organizational and social issues within software development communities that often lead to additional project costs and reduced software quality. Previous research identified a variety of community smells that are connected to sub-optimal patterns under different perspectives of organizational-social structures in the software development community. To detect community smells and understanding the characteristics of such organizational-social structures in a project, we propose csDetector, an open source tool that is able to automatically detect community smells within a project and provide relevant socio-technical metrics. csDetector uses a machine learning based detection approach that learns from various existing bad community development practices to provide automated support in detecting related community smells. We evaluate the effectiveness of csDetector on a benchmark of 143 open source projects from GitHub. Our results show that the csDetector tool can detect ten commonly occurring community smells in open software projects with an average F1 score of 84%. csDetector is publicly available, with a demo video, at: https://github.com/Nuri22/csDetector.
DOI 原文 ·
@inproceedings{paperbot6446,
title = {csDetector: an open source tool for community smells detection},
author = {Nuri Almarimi and Ali Ouni and Moataz Chouchen and Mohamed Wiem Mkaouer},
booktitle = {FSE},
year = {2021},
doi = {10.1145/3468264.3473121}
}