Massive data from software repositories and collaboration tools is widely used to study social aspects in software development. One question that has been addressed by a number of recent works is how the size and structure of a software project influence team productivity, a question famously considered in Brooks’ law. A number of recent studies using massive repository data suggest that developers in larger teams tend to be less productive compared to smaller teams. Despite using similar methods and data, other studies argue for a positive linear or super-linear relationship between team size and productivity, thus contesting the view of software economics that software projects are diseconomies of scale.

In our work, we study epistemological challenges that can explain the disagreement between recent studies of developer productivity in massive repository data. We further provide—to the best of our knowledge—the largest, curated corpus of GitHub projects tailored to investigate the influence of team size and collaboration patterns on individual and collective productivity. Our work contributes to the ongoing discussion on the choice of productivity metrics in the operationalization of hypotheses about determinants of successful software projects. It further highlights general pitfalls in the analysis of big data and shows that the use of bigger data sets does not automatically lead to more reliable insights.