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Git repositories are an important source of empirical software engineering product and process data. Running the Git command-line tool and processing its output with other Unix tools allows the incremental construction of sophisticated data processing pipelines. Git data analytics on the command-line can be systematically presented through a pattern that involves fetching, selection, processing, summarization, and reporting. For each part of the processing pipeline, we examine the tools and techniques that can be most effectively used to perform the task at hand. The presented techniques can be easily applied, first to get a feeling of version control repository data at hand and then also for extracting empirical results. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]