Mining Software Engineering Data from GitHub

被引：31

作者：

Gousios, Georgios ^{[1
]}

Spinellis, Diomidis ^{[2
]}

机构：

[1] Delft Univ Technol, Dept Software Technol, Delft, Netherlands

[2] Athens Univ Econ & Business, Dept Management Sci & Technol, Athens, Greece

来源：

PROCEEDINGS OF THE 2017 IEEE/ACM 39TH INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING COMPANION (ICSE-C 2017) | 2017年

关键词：

GitHub; GHTorrent; empirical software engineering; Git;

D O I：

10.1109/ICSE-C.2017.164

中图分类号：

TP31 [计算机软件];

学科分类号：

081202 ; 0835 ;

摘要：

GitHub is the largest collaborative source code hosting site built on top of the Git version control system. The availability of a comprehensive API has made GitHub a target for many software engineering and online collaboration research efforts. In our work, we have discovered that a) obtaining data from GitHub is not trivial, b) the data may not be suitable for all types of research, and c) improper use can lead to biased results. In this tutorial, we analyze how data from GitHub can be used for large-scale, quantitative research, while avoiding common pitfalls. We use the GHTorrent dataset, a queryable offline mirror of the GitHub API data, to draw examples from and present pitfall avoidance strategies.

引用

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页码：501 / 502

页数：2

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