Replication Package of the study "Exploring the Notion of Risk in Reviewer Recommendation"

Description This is a Dockerized version of the repository https://github.com/software-rebels/RAR_Recommender . The package is developed to ease the replication of our study for researchers and practitioners. This package contains the necessary information to replicate the study "Exploring the Notion of Risk in Reviewer Recommendation." This code extends the RelationalGit package (https://github.com/CESEL/RelationalGit) from the study of E. Mirsaeedi and P. C. Rigby [1]. It adds some functionality needed to incorporate the concept of the fix-inducing likelihood of a project. In addition to our dataset, this repository also has the supporting materials for our study. The supporting materials are in the "ICSME_online_materials_ICSME.pdf" and contain the following items: Table 1 contains the detail of the dataset and some related statistics for each studied project. Table 2 shows the risk measures used in our defect prediction model. We use Commit Guru Tool to extract the data from the GitHub repositories and then use this data to train our defect prediction model. Figure 1 illustrates the distribution of predicted defect probability of different projects. This distribution shows how the defect probability of different periods is similar to the adjacent periods. Replication Steps: Read Requirements.md and ensure the necessary software/hardware requirements. Read Install.md and install the necessary packages to run the containers. Follow the steps in ReadMe.md to reproduce the results of our study. References: [1] E. Mirsaeedi and P. C. Rigby, 'Mitigating turnover with code review recommendation: Balancing expertise, workload, and knowledge distribution', στο Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering, 2020.本仓库为https://github.com/software-rebels/RAR_Recommender 的Docker容器化版本。本工具包旨在为研究者与从业者简化本研究的复现流程，包含复现论文《探索审稿人推荐中的风险概念》（Exploring the Notion of Risk in Reviewer Recommendation）所需的全部必要资料。 本代码基于E. Mirsaeedi与P. C. Rigby[1]的研究中提出的RelationalGit包（仓库地址：https://github.com/CESEL/RelationalGit）进行扩展，新增了用于纳入项目fix-inducing likelihood（缺陷诱导修复概率）相关概念的功能。 除本研究的数据集外，本仓库还附带本研究的配套支撑材料，相关内容存放于文件"ICSME_online_materials_ICSME.pdf"中，具体包含以下内容： 表1 详述了本研究涉及的数据集详情，以及各研究项目的相关统计指标； 表2 列出了我们的缺陷预测模型中所采用的风险度量方法。 我们使用Commit Guru工具从GitHub代码仓库中提取数据，并基于提取得到的数据训练缺陷预测模型。图1 展示了不同项目的预测缺陷概率分布情况，该分布结果表明，不同时段的缺陷概率分布特征与相邻时段的分布特征具有较高相似性。 复现步骤： 1. 阅读Requirements.md文档，确认本项目所需的软件与硬件配置要求； 2. 阅读Install.md文档，安装运行容器所需的全部依赖包； 3. 按照ReadMe.md文档中的操作步骤执行，即可复现本研究的实验结果。 参考文献： [1] E. Mirsaeedi 与 P. C. Rigby. 借助代码评审推荐缓解人员流动：平衡专业能力、工作负载与知识分布[C]//ACM/IEEE第42届国际软件工程会议论文集. 2020.