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Predictive models for software projects' characteristics have beentraditionally based on project-level metrics, employing only littledeveloper-level information, or none at all. In this work we suggest novelmetrics that capture temporal and semantic developer-level informationcollected on a per developer basis. To address the scalability challengesinvolved in computing these metrics for each and every developer for a largenumber of source code repositories, we have built a designated repositorymining platform. This platform was used to create a metrics dataset based onprocessing nearly 1000 highly popular open source GitHub repositories,consisting of 147 million LOC, and maintained by 30,000 developers. Thecomputed metrics were then employed to predict the corrective, perfective, andadaptive maintenance activity profiles identified in previous works. Ourresults show both strong correlation and promising predictive power withR-squared values of 0.83, 0.64, and 0.75. We also show how these results mayhelp project managers to detect anomalies in the development process and tobuild better development teams. In addition, the platform we built has thepotential to yield further predictive models leveraging developer-level metricsat scale.

Using Temporal and Semantic Developer-Level Information to Predict Maintenance Activity Profiles