Towards Analyzing Contributions from Software Repositories to Optimize Issue Assignment

2020 IEEE 20th International Conference on Software Quality, Reliability and Security (QRS) Pub Date : 2020-12-01 DOI:10.1109/QRS51102.2020.00042

Vasileios Matsoukas, Themistoklis G. Diamantopoulos, Michail D. Papamichail, A. Symeonidis

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引用次数: 5

Abstract

Most software teams nowadays host their projects online and monitor software development in the form of issues/tasks. This process entails communicating through comments and reporting progress through commits and closing issues. In this context, assigning new issues, tasks or bugs to the most suitable contributor largely improves efficiency. Thus, several automated issue assignment approaches have been proposed, which however have major limitations. Most systems focus only on assigning bugs using textual data, are limited to projects explicitly using bug tracking systems, and may require manually tuning parameters per project. In this work, we build an automated issue assignment system for GitHub, taking into account the commits and issues of the repository under analysis. Our system aggregates feature probabilities using a neural network that adapts to each project, thus not requiring manual parameter tuning. Upon evaluating our methodology, we conclude that it can be efficient for automated issue assignment.

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分析软件库贡献以优化问题分配

如今，大多数软件团队在线托管他们的项目，并以问题/任务的形式监控软件开发。这个过程需要通过评论进行沟通，并通过提交和关闭问题报告进度。在这种情况下，将新问题、任务或bug分配给最合适的贡献者可以极大地提高效率。因此，已经提出了几种自动化问题分配方法，但是它们都有很大的局限性。大多数系统只关注于使用文本数据分配bug，仅限于明确使用bug跟踪系统的项目，并且可能需要手动调整每个项目的参数。在这项工作中，我们为GitHub构建了一个自动问题分配系统，考虑到正在分析的存储库的提交和问题。我们的系统使用适应每个项目的神经网络聚合特征概率，因此不需要手动调整参数。在评估我们的方法后，我们得出结论，它对于自动化问题分配是有效的。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

2020 IEEE 20th International Conference on Software Quality, Reliability and Security (QRS)

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