Continued timely maintenance is a key aspect of project security, but typically requires in-depth knowledge of a project’s code base. Truck Factor is a metric that aims to represent how vulnerable a project is to losing this knowledge through the attrition of key contributors. However, the accuracy of existing Truck Factor estimators scales poorly with project size since they tend to ignore influential team members in managerial roles, which are more common in large projects.

This work proposes SNet, a novel socially aware Truck Factor estimator based on social network analysis. SNet uses network centrality measures and social signals such as GitHub Issue interactions to estimate Truck Factor and identify Truck Factor contributors. We evaluate SNet against an existing ground truth comprised of twenty-six open source projects. Our social network analysis approach achieves superior contributor classification performance (Median F1 score = 0.8) while reducing computation time by over 2x compared to state-of-the-art estimators.