Identifying GitHub trends using temporal analysis

Master Thesis
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Abstract

With the increase of remote work due to COVID-19 and the overall movement towards open source projects, distributed version control system, like Git gained popularity overthe last years. The publicly available data on platforms (e.g., GitHub) therefore becomes richer and attracts sociologists and software analysts for further analysis.This master thesis aims to visualize GitHub trends using Visual Analytics. The data used originates from the GitHub API as well as GitHub Archive, is multivariate and contains different types of information containing repositories, users and events. This data will be extended by the temporal dimension to identify potential trends. For the problem definition and further methodology, the design triangle as described by Mikschet. al is being used.The outcome of the thesis is a prototype, that not only enables domain experts to fulfill common tasks related to identifying GitHub anomalies and trends but also allows foruser interaction to focus on more granular analysis. While many trends can potentially be visualized, this thesis will focus on a small subset of trends to introduce a generic approach and evaluate it on given scenarios and tasks. The general group of potential user groups is broad, but there is a strong emphasis on analysts in technology industries.The prototype was evaluated with domain experts in different fields of expertise that were asked to perform given tasks that can be fulfilled using the developed prototype. The results of the evaluation showed, that there is a strong interest in the analysis of GitHub data and that the right encodings and visualization methods can help find patterns and trends significantly.

Year of Publication
2021
Secondary Title
Institute of Visual Computing and Human-Centered Technology
Number of Pages
68
Publisher
TU Wien
Place Published
Vienna
DOI
10.34726/hss.2021.93182
reposiTUm Handle
Paper
TU Wien Library AC16386639
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