Context

: Addressing user requests in the form of bug reports and Github issues represents a crucial task of any successful software project. However, user-submitted issue reports tend to widely differ in their quality, and developers spend a considerable amount of time handling them.

Objective

: By collecting a dataset of around 6,000 issues of 279 GitHub projects, we observe that developers take significant time (i.e., about five months, on average) before labeling an issue as a wontfix. For this reason, in this paper, we empirically investigate the nature of wontfix issues and methods to facilitate issue management process.

Method

: We first manually analyze a sample of 667 wontfix issues, extracted from heterogeneous projects, investigating the common reasons behind a “wontfix decision”, the main characteristics of wontfix issues and the potential factors that could be connected with the time to close them. Furthermore, we experiment with approaches enabling the prediction of wontfix issues by analyzing the titles and descriptions of reported issues when submitted.

Results and conclusion

: Our investigation sheds some light on the wontfix issues’ characteristics, as well as the potential factors that may affect the time required to make a “wontfix decision”. Our results also demonstrate that it is possible to perform prediction of wontfix issues with high average values of precision, recall, and F-measure (90%–93%).

中文翻译：

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“我们不会解决这个问题吗？” 在 GitHub 上对 wontfix 问题进行定性表征和自动识别

语境

：以错误报告和 Github 问题的形式解决用户请求是任何成功软件项目的关键任务。但是，用户提交的问题报告的质量往往差别很大，开发人员会花费大量时间来处理它们。

客观的

：通过收集 279 个 GitHub 项目的大约 6,000 个问题的数据集，我们观察到开发人员在将问题标记为 wontfix 之前花费了大量时间（即平均大约五个月）。出于这个原因，在本文中，我们实证研究了 wontfix 问题的性质和方法，以促进问题管理过程。

方法

：我们首先手动分析了从异构项目中提取的 667 个 wontfix 问题的样本，调查了“wontfix 决策”背后的常见原因、wontfix 问题的主要特征以及可能与关闭它们的时间相关的潜在因素。此外，我们通过分析提交时报告的问题的标题和描述来试验能够预测 wontfix 问题的方法。

结果与结论

：我们的调查揭示了 wontfix 问题的特征，以及可能影响做出“wontfix 决定”所需时间的潜在因素。我们的结果还表明，可以使用精度、召回率和 F 度量（90%–93%）的高平均值来预测不会修复的问题。