Aug 29, 2023
How can we determine the influence of companies in open source software development, and which metrics provide information about the ecosystem of an open source project?
In this blog post we want to give you some insights into the Bachelor thesis of our team member Matthias Kopeinig. The primary goal of his work is to focus on quantifying the contributions of companies to the open source software ecosystem and to develop analytical methods and metrics to identify companies’ impact in open source projects.
In the context of this work, the question "how the influence of companies in open source software development can be determined and which metrics provide information about the ecosystem of an open source project?" is explored. To answer this research question, the following aspects are researched:
When researching suitable and quantifiable metrics, the fundamental question is which criteria are best suited to show company influences and to what extent these can be covered using the available characteristics and data. In this context, the majority of the metrics used were obtained from the Chaoss community, as this project provides a well-suited basis for evaluating open source projects from different areas and perspectives.
In the following, we give a description of the most important metrics covered in the analysis:
To obtain a sample of GitHub repositories, a list of the 10 most popular programming languages, measured by the number of repositories, was compiled. This ensures that the analysis is applied to different technologies. A total of 100 repositories were selected as sample, with 10 repositories with the highest star count of each of the 10 languages with the highest number of repositories. In total, the sample comprises 175k commits with 12k contributors.
The following figure presents a bar chart grouping the contributions of big tech companies compared to other companies in open source projects. Commits from unidentifiable companies were not included.
Of all the contributors identified, those from big tech companies account for 41.1%, while the remaining 58.9% are from various other companies. The proportion of commits made by big tech companies accounts for 61.8% of all commits, while various other companies are responsible for 38.2% of commits. The two charts show interesting differences in the level of contribution by employees of tech companies. Although there are fewer contributors from big tech companies in the sample, their number of commits predominates, suggesting a more intensive contribution by people from the big tech environment.
The evaluation of the metric "Organisational Influence" within the sample shows that for 30% of the repositories no organisational structures in relation to the company of the participants can be shown. Only one repository was identified in the sample whose development was carried out entirely within one organisation (Microsoft).
The organisational influence in relation to individual companies shows that Microsoft, for example, represents an average of 2.5% of the community within the repositories from the sample. Across all five of the big tech companies (Microsoft, Google, Meta, Amazon, Apple), the cumulative share is 5.5%. It can thus be concluded that, in terms of active participation through commits, these big tech companies account for an average of about 5 to 6 percent of the open source community of a repository on GitHub.
In summary, Matthias' work shows the following main points: