If you've ever opened a GitHub repository with hundreds—or even thousands—of issues, you've probably experienced the same feeling I did. Where do you even begin? At first glance, GitHub Issues look like an endless list of bug reports, feature requests, enhancement proposals, questions, and pull requests. Reading them one by one quickly becomes overwhelming. When I started my developer experience research, I thought collecting GitHub issues would be the easy part.
I was wrong. The real challenge wasn't reading the issues. The real challenge was organizing them into research data and make that data understandable to maintainers, co-UX designers for analysis as well the engineers who wanted to explore the raw data.
After spending months analyzing GitHub repositories, I realized that the quality of your research depends far more on how you organize the data than on how many issues you collect. A spreadsheet full of issue links is not research. A structured dataset is.
My First Spreadsheet Failed
Like many researchers, I started with a simple spreadsheet.
