TensorFlow开源软件社区中贡献修订的实证研究

Empirical Study on Pull-request Revisions in Open Source Software Community of TensorFlow

人工智能(artificial intelligence, AI)的飞速发展得益于开源社区的开放协同,大量的开发者通过提交PR(pull-request)为AI开源软件做贡献.然而,外部贡献者所提交的PR质量参差不齐,开源项目管理团队需要对PR进行代码审查,并要求贡献者根据审查意见对PR进行修订. PR的修订过程对AI开源软件的质量有着重要的影响,因此对该过程进行更加全面、深入的实证研究很有必要.首先,从TensorFlow开源软件社区中收集一组PR的修订历史,通过对PR的代码提交信息以及审查评论进行定性分析,归纳总结PR修订类型的分类体系.其次,根据此分类体系人工标注一组修订数据集,并基于此数据集定量分析不同修订类型的频率分布、次序分布以及关联关系.研究结果表明:TensorFlow开源社区中的PR存在3大类共11种不同类型的修订,其中完善类修订出现的频率最高;此外,相比于其他类修订和完善类修订,修正类修订更常发生在PR的早期更新中;与结构相关的修订更有可能与其他类型的修订同现或邻现,配置修订以及变基修订有较大概率会接连出现.实证研究结果可帮助AI开源实践者和研究者更好地理解PR的修订过程,特别是有助于引导PR的审查和修订行为、提高开源群体协同效率.

The recent boom in artificial intelligence(AI)benefits from the open collaboration of the open source software(OSS)community.An increasing number of OSS developers are contributing to AI projects by submitting pull requests(PRs).However,the PR quality submitted by external contributors varies,and the AI project management teams have to review PRs and ask contributors to revise them if necessary.Since the revision exerts a direct impact on the review efficiency and acceptance of PRs,it is important to achieve a better understanding of PR revisions.This study conducts an empirical study based on a set of PRs and their revision histories collected from the TensorFlow project.It first manually analyzes a sample of commit messages,reviews PR comments,and constructs a taxonomy of revision types.Then,according to the defined taxonomy,a large set of PR revisions are manually labeled.Based on the dataset,the frequency and order of each type of revision are explored.Additionally,this study also investigates the frequency distribution,order distribution,and correlation relationship between different types of revisions.The empirical findings show that there are 11 different types of revisions which can be classified into three categories.Evolvability revisions occur more frequently than other revision types,and functional revisions are more likely to occur in the early PR updates than evolvability revisions and other types of revisions.Structurerelated revisions have a high chance to co-occur or adj-occur with other revisions.Configuration-related revisions or rebasing revisions are more likely to appear in succession.The empirical results can help AI open source practitioners and researchers better understand the PR revision process,especially guide the review and revision behaviors of PRs and improve the collaborative efficiency of open source groups.