Long-term active integrator prediction in the evaluation of code contributions

In open source software (OSS) projects, integrators are given high-level access to repositories so that they could maintain and manage projects. Although integrators play a critical role in evaluating code changes for OSS projects, they may be short-term active. Long-term active integrators keep in evaluating code update submission and managing responses from contributors. In order to survive and succeed, OSS projects need to attract and retain long-term active integrators. To assist OSS projects to retain active integrators, we propose a method called LTAPredict to predict whether integrators will be longterm active in the evaluation of code contributions. LTAPredict collects activity data of integrators, extracts a rich set of features, and makes prediction via machine learning techniques. We perform experiments on 37 popular projects, containing a total of 1,073 integrators. Results show that based on the Decision Tree, LTAPredict achieves the accuracy as 0.829, the precision as 0.81, the recall as 0.827 and the F1 as 0.818. Meanwhile, we evaluate the feature importance to identify the most significant indicators of long-term active integrators. We observe that whether integrators becoming long-term active is associated with the number of active months and social distance with contributors in their first year as integrators. These findings assist OSS projects to identify potential long-term active integrators and adopt better strategies to retain them in the evaluation of code contributions.