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A list of 12,442 GitHub Projects with Truck Factor = 1

A list of 12,442 GitHub Projects with Truck Factor =1

Truck Factor (aka bus factor or lottery number) designates the minimal number of developers that must leave (i.e., get hit by a truck or bus, or win in the lottery) before a project becomes unsustainable. Therefore, TF measures the dependency of a project on a small number of members.

Last year, we proposed an algorithm to estimate the truck factor of software projects, using data from version control systems. We used the algorithm to compute the truck factor of 133 popular GitHub systems. The algorithm was first published in the form of a preprint at PeerJ, which attracted a lot of interest (it has 8,600 downloads). We also published a paper at ICPC 2016.

Now, we implemented a web-based tool, called GitTrends, which estimates the truck factor for 17,284 GitHub projects.

http://gittrends.io

To check the list of projects with TF=1, as estimated by GitTrends, check this page:

http://gittrends.io/#/explore?page=1&language=All&domain=All&growth=All&min_tf=1&max_tf=1

As you will see, 12,442 projects have TF = 1, which corresponds to 72% of the projects monitored by GitTrends.

The top-10 with highest number of stars and with TF=1 are:

  1. FreeCodeCamp
  2. Free-Programming-books
  3. d3
  4. You-Dont-know-JS
  5. Font-Awesome
  6. electron
  7. vue
  8. animate.css
  9. reveal.js
  10. Semantic-UI

Important notes:

  1. The results provided by GitTrends are an estimation, based on code authorship measures. As recommend to all software metrics, TF results should not be used without proper interpretation.
  2. Good software engineering practices certainly contribute to overcome truck factor episodes (documentation, tests, etc; see again our previous paper). However, as an automatic tool, GitTrends does not consider these practices to estimate TFs.
  3. In case the TF developers leave a project, this does not necessarily mean the project will be discontinued, but that its maintenance and evolution will be in trouble. For example, bugs might take more time to get fixed and new features might take more time to be implemented.

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