Mining information on software projects hosted on GitHub can reveal a lot of useful information on how software projects (and the community behind them) should be managed to optimize your chances of success
Some stats on what we have accomplished in these seven years online plus the five posts that got the most visits in one single day ever
Meta-analysis of 93 research papers reporting findings based on mining GitHub repositories. We report concerns on several aspects: dataset collection, replicability, sampling techniques and methodologies.
First empirical study on the adoption of Eclipse-based modelling technologies in open-source projects on GitHub. Promising results!
Contribute to our glossary of software modeling / model-driven terms . You can do it through the github repostory created to support this effort.
What makes an open source software successful (in terms of commits, contributors,...)? I have no idea but this post explains one thing that will NOT work
What can issue labels teach you about your project? Three simple yet powerful visualizations to better understand WHO is doing WHAT
Check (and contribute to) the OCL repository in GitHub
Create your own IFML (Interaction Flow Modeling Language) diagrams thanks to our free Eclipse editor for IFML.
Did you know that more than one milion GitHub projects have less than 10 commits? And that over 2M have basically zero external contributions?
Which labels are most used in GitHub projects? Do projects use labels or just ignore them? Some facts based on the automatic analysis of GitHub projects
We already discussed that releasing a tool as open source does not guarantee that people will