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arXiv 2103.12653cs.SEcs.LG
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我能解决它吗?识别完成 OSS 任务所需的 API

Can I Solve It? Identifying APIs Required to Complete OSS Task

Northern Arizona University(北亚利桑那大学);Universidade Tecnológica Federal do Paraná(巴拉那联邦理工大学);Oregon State University(俄勒冈州立大学)

Fabio Santos, Igor Wiese, Bianca Trinkenreich, Igor Steinmacher, Anita Sarma, Marco Gerosa

AI总结:

本文研究利用问题描述和项目历史自动为 OSS 问题标注所需 API 领域标签,模型精确率最高达 82%、召回率最高达 97.8%,用户研究表明该标签比现有架构标签更有助于贡献者选择任务。

摘要:

开源软件项目会为未解决的问题添加标签,以帮助贡献者选择任务。然而,手动为问题添加标签既耗时又容易出错。当前自动创建标签的方法大多局限于将问题分类为缺陷或非缺陷。在本文中,我们研究了使用完成任务所需 API 的领域来标记问题的可行性和相关性。我们利用问题描述和项目历史来构建预测模型,其精确率最高达到 82%,召回率最高达到 97.8%。我们还进行了一项用户研究(n=74),以评估这些标签对潜在贡献者的相关性。结果表明,这些标签对参与者选择任务是有用的,并且 API 领域标签比现有的基于架构的标签被更频繁地选择。我们的结果可以启发自动标记问题工具的创建,帮助开发者找到更符合其技能的任务。

英文摘要:

Open Source Software projects add labels to open issues to help contributors choose tasks. However, manually labeling issues is time-consuming and error-prone. Current automatic approaches for creating labels are mostly limited to classifying issues as a bug/non-bug. In this paper, we investigate the feasibility and relevance of labeling issues with the domain of the APIs required to complete the tasks. We leverage the issues' description and the project history to build prediction models, which resulted in precision up to 82% and recall up to 97.8%. We also ran a user study (n=74) to assess these labels' relevancy to potential contributors. The results show that the labels were useful to participants in choosing tasks, and the API-domain labels were selected more often than the existing architecture-based labels. Our results can inspire the creation of tools to automatically label issues, helping developers to find tasks that better match their skills.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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