Abstract
Git metadata contains rich information for developers to understand the overall context of a large software development project. Thus it can help new developers, managers, and testers understand the history of development without needing to dig into a large pile of unfamiliar source code. However, the current tools for Git visualization are not adequate to analyze and explore the metadata: They focus mainly on improving the usability of Git commands instead of on helping users understand the development history. Furthermore, they do not scale for large and complex Git commit graphs, which can play an important role in understanding the overall development history. In this paper, we present Githru, an interactive visual analytics system that enables developers to effectively understand the context of development history through the interactive exploration of Git metadata. We design an interactive visual encoding idiom to represent a large Git graph in a scalable manner while preserving the topological structures in the Git graph. To enable scalable exploration of a large Git commit graph, we propose novel techniques (graph reconstruction, clustering, and Context-Preserving Squash Merge (CSM) methods) to abstract a large-scale Git commit graph. Based on these Git commit graph abstraction techniques, Githru provides an interactive summary view to help users gain an overview of the development history and a comparison view in which users can compare different clusters of commits. The efficacy of Githru has been demonstrated by case studies with domain experts using real-world, in-house datasets from a large software development team at a major international IT company. A controlled user study with 12 developers comparing Githru to previous tools also confirms the effectiveness of Githru in terms of task completion time.
Highlights
The Git repository archives the development history of a project
We propose novel analytic techniques to deal with the complexity and scalability issues of large sets of Git metadata
We propose a set of new analytics techniques to abstract large and complex Git commit graphs
Summary
Git metadata are a collection of content for each commit, such as its unique ID, author, message, date, and information on the set of changed files. The complexity of a DAG with Git metadata mainly depends on the edges created through branching in Git. Branching refers to diverging from the main line of development and continuing to work without affecting that main line. In Git, a branch is a lightweight movable pointer to a commit, and branches are cheap to create and destroy [22]. Due to this low cost, Git encourages a workflow that branches and merges often
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