  Predictive models for software projects' characteristics have been
traditionally based on project-level metrics, employing only little
developer-level information, or none at all. In this work we suggest novel
metrics that capture temporal and semantic developer-level information
collected on a per developer basis. To address the scalability challenges
involved in computing these metrics for each and every developer for a large
number of source code repositories, we have built a designated repository
mining platform. This platform was used to create a metrics dataset based on
processing nearly 1000 highly popular open source GitHub repositories,
consisting of 147 million LOC, and maintained by 30,000 developers. The
computed metrics were then employed to predict the corrective, perfective, and
adaptive maintenance activity profiles identified in previous works. Our
results show both strong correlation and promising predictive power with
R-squared values of 0.83, 0.64, and 0.75. We also show how these results may
help project managers to detect anomalies in the development process and to
build better development teams. In addition, the platform we built has the
potential to yield further predictive models leveraging developer-level metrics
at scale.
