10 Years Bug-Fix Dataset (PROMISE'19)

Replication Package of the paper "From Reports to Bug-Fix Commits: A 10 Years Dataset of Bug-Fixing Activity from 55 Apache's Open Source Projects"<br><br><b>ABSTRACT:</b><br>Bugs appear in almost any software development. Solving all or at least a large part of them requires a great deal of time, effort, and budget. Software projects typically use issue tracking systems as a way to report and monitor bug-fixing tasks. In recent years, several researchers have been conducting bug tracking analysis to better understand the problem and thus provide means to reduce costs and improve the efficiency of the bug-fixing task. In this paper, we introduce a new dataset composed of more than 70,000 bug-fix reports from 10 years of bug-fixing activity of 55 projects from the Apache Software Foundation, distributed in 9 categories. We have mined this information from Jira issue track system concerning two different perspectives of reports with closed/resolved status: static (the latest version of reports) and dynamic (the changes that have occurred in reports over time). We also extract information from the commits (if they exist) that fix such bugs from their respective version-control system (Git).We also provide a change analysis that occurs in the reports as a way of illustrating and characterizing the proposed dataset. Once the data extraction process is an error-prone nontrivial task, we believe such initiatives like this could be useful to support researchers in further more detailed investigations.<br><br>You can find the full paper at: https://doi.org/10.1145/3345629.3345639<br>If you use this dataset for your research, please reference the following paper:<pre><br>@inproceedings{Vieira:2019:RBC:3345629.3345639, author = {Vieira, Renan and da Silva, Ant\^{o}nio and Rocha, Lincoln and Gomes, Jo\~{a}o Paulo}, title = {From Reports to Bug-Fix Commits: A 10 Years Dataset of Bug-Fixing Activity from 55 Apache's Open Source Projects}, booktitle = {Proceedings of the Fifteenth International Conference on Predictive Models and Data Analytics in Software Engineering}, series = {PROMISE'19}, year = {2019}, isbn = {978-1-4503-7233-6}, location = {Recife, Brazil}, pages = {80--89}, numpages = {10}, url = {http://doi.acm.org/10.1145/3345629.3345639}, doi = {10.1145/3345629.3345639}, acmid = {3345639}, publisher = {ACM}, address = {New York, NY, USA}, keywords = {Bug-Fix Dataset, Mining Software Repositories, Software Traceability}, } <br> </pre>论文《从报告到缺陷修复提交：来自55个Apache开源项目10年缺陷修复活动的十年数据集》的复现包<br><br><b>摘要：</b><br>几乎所有软件开发过程中都会出现软件缺陷（bug）。解决全部或至少大部分缺陷需要耗费大量时间、人力与预算成本。软件项目通常借助问题追踪系统（issue tracking system）来上报并监控缺陷修复任务。近年来，诸多研究者开展缺陷追踪分析研究，以更深入地理解该问题，进而提出降低成本、提升缺陷修复任务效率的解决方案。<br><br>本文提出了一套全新的数据集（dataset），该数据集涵盖了Apache软件基金会（Apache Software Foundation）旗下55个项目十年间的缺陷修复活动相关的7万余份缺陷修复报告，并将其划分为9个类别。我们从Jira问题追踪系统中挖掘了该数据集的相关信息，针对状态为已关闭/已解决的报告，从两个视角进行采集：静态视角（报告的最新版本）与动态视角（报告随时间推移发生的变更）。此外，我们还从对应版本控制系统（version-control system）中提取了修复上述缺陷的提交（commit）信息（若存在相关提交）。<br><br>同时，我们针对报告中的变更情况开展了分析，以阐释并刻画本数据集的特征。鉴于数据抽取过程是一项极易出错且颇具难度的工作，我们认为本数据集这类资源可为研究者开展更深入的细化研究提供有力支撑。<br><br>您可通过以下链接获取论文全文：https://doi.org/10.1145/3345629.3345639<br><br>若您在研究中使用本数据集，请引用以下论文：<pre><br>@inproceedings{Vieira:2019:RBC:3345629.3345639, author = {Vieira, Renan and da Silva, Ant^{o}nio and Rocha, Lincoln and Gomes, Jo~{a}o Paulo}, title = {From Reports to Bug-Fix Commits: A 10 Years Dataset of Bug-Fixing Activity from 55 Apache's Open Source Projects}, booktitle = {Proceedings of the Fifteenth International Conference on Predictive Models and Data Analytics in Software Engineering}, series = {PROMISE'19}, year = {2019}, isbn = {978-1-4503-7233-6}, location = {Recife, Brazil}, pages = {80--89}, numpages = {10}, url = {http://doi.acm.org/10.1145/3345629.3345639}, doi = {10.1145/3345629.3345639}, acmid = {3345639}, publisher = {ACM}, address = {New York, NY, USA}, keywords = {Bug-Fix Dataset, Mining Software Repositories, Software Traceability}, } <br> </pre>