RepoQuester:一个评估GitHub项目的工具

Kowndinya Boyalakuntla, M. Nagappan, S. Chimalakonda, Nuthan Munaiah
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引用次数: 0

摘要

考虑到GitHub上存储库的急剧增加,开发人员通常很难找到满足他们需求的相关项目,因为分析源代码和其他工件是非常费力的。在我们之前的工作中,我们提出了Repo Reaper(或简称Reaper),它基于跨越项目协作、质量和维护的七个指标来评估GitHub项目。Reaper通过将近180万个项目分为“工程”和“非工程”软件项目,确定了140万个项目没有协作或软件开发的目的。虽然Reaper可以用来评估基于GHTorrent的数百万个存储库,但它不是为开发人员在本地机器上使用独立存储库而设计的,它依赖于GHTorrent。因此,在本文中,我们提出了一个重新设计和扩展的命令行工具RepoQuester,旨在帮助开发人员在本地机器上评估GitHub项目。RepoQuester为项目计算度量,并且不会将项目分为“工程化”和“非工程化”。然而,为了证明RepoQuester生成的度量分数的正确性,我们已经在Reaper的训练和验证数据集上执行了项目分类,方法是用最新的度量分数(由RepoQuester报告)更新它们。这些数据集都是人工建立的。在分析过程中,我们观察到建立在更新数据集上的机器学习分类器产生了72%的F1分数。在评估期间,对于每个项目,我们发现RepoQuester可以在不到10秒的时间内分析度量分数。解释该工具重点和用法的演示视频可在https://youtu.be/Q8OdmNzUfN0上获得,源代码可在https://github.com/Kowndinya2000/Repoquester上获得。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
RepoQuester: A Tool Towards Evaluating GitHub Projects
Given the drastic rise of repositories on GitHub, it is often hard for developers to find relevant projects meeting their requirements as analyzing source code and other artifacts is effort-intensive. In our prior work, we proposed Repo Reaper (or simply Reaper) that assesses GitHub projects based on seven metrics spanning across project collaboration, quality, and maintenance. Reaper identified 1.4 million projects out of nearly 1.8 million projects to have no purpose for collaboration or software development by classifying them into ‘engineered’ and ‘non-engineered’ software projects. While Reaper can be used to assess millions of repositories based on GHTorrent, it is not designed to be used by developers for standalone repositories on local machines and is dependent on GHTorrent. Hence, in this paper, we propose a re-engineered and extended command-line tool named RepoQuester that aims to assist developers in evaluating GitHub projects on their local machines. RepoQuester computes metrics for projects and does not classify projects into ‘engineered’ and ‘non-engineered’ ones. However, to demonstrate the correctness of metric scores produced by RepoQuester, we have performed the project classification on the Reaper’s training and validation datasets by updating them with the latest metric scores (as reported by RepoQuester). These datasets have their ground truth manually established. During the analysis, we observed that the machine learning classifiers built on the updated datasets produced an F1 score of 72%. During the evaluation, for each project, we found that RepoQuester can analyze metric scores in less than 10 seconds. A demo video explaining the tool highlights and usage is available at https://youtu.be/Q8OdmNzUfN0, and source code at https://github.com/Kowndinya2000/Repoquester.
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