JIRA存储库数据集:理解软件开发的社会方面

Marco Ortu, Giuseppe Destefanis, Bram Adams, Alessandro Murgia, M. Marchesi, R. Tonelli
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引用次数: 69

摘要

问题跟踪系统存储有价值的数据,用于测试有关维护的假设,建立统计预测模型,以及最近调查开发人员的“有效性”。特别是,Jira问题跟踪系统是一个专有的跟踪系统,在过去几年中获得了巨大的普及,并提供了独特的功能,如项目管理系统和Jira敏捷看板。本文介绍了从四个流行的开源生态系统(以及用于提取的工具和基础设施)Apache Software Foundation、Spring、JBoss和CodeHaus社区的Jira ITS中提取的数据集。我们的数据集拥有超过1K个项目,包含超过700K个问题报告和超过200万条问题评论。使用这些数据,我们能够深入研究开发人员之间的沟通过程,以及这方面如何影响开发过程。此外,开发人员发布的评论不仅包含技术信息,还包含有关情绪和情感的有价值的信息。由于情感分析和软件工程中的人的方面在过去几年中变得越来越重要,我们希望通过这个存储库鼓励在这个方向上进一步的研究。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The JIRA Repository Dataset: Understanding Social Aspects of Software Development
Issue tracking systems store valuable data for testing hypotheses concerning maintenance, building statistical prediction models and recently investigating developers "affectiveness". In particular, the Jira Issue Tracking System is a proprietary tracking system that has gained a tremendous popularity in the last years and offers unique features like the project management system and the Jira agile kanban board. This paper presents a dataset extracted from the Jira ITS of four popular open source ecosystems (as well as the tools and infrastructure used for extraction) the Apache Software Foundation, Spring, JBoss and CodeHaus communities. Our dataset hosts more than 1K projects, containing more than 700K issue reports and more than 2 million issue comments. Using this data, we have been able to deeply study the communication process among developers, and how this aspect affects the development process. Furthermore, comments posted by developers contain not only technical information, but also valuable information about sentiments and emotions. Since sentiment analysis and human aspects in software engineering are gaining more and more importance in the last years, with this repository we would like to encourage further studies in this direction.
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