Whom Are You Going to Call?: Determinants of @-Mentions in GitHub Discussions

TL;DR

This study investigates the socio-technical factors influencing @-mentions in GitHub discussions, developing models to predict whom to call on for tasks, revealing key attributes like visibility, expertise, and productivity.

Contribution

The paper introduces predictive models for @-mentions based on observable developer attributes, highlighting their cross-project applicability and differences across projects.

Findings

Visibility, expertise, and productivity increase @-mentions.

Responsiveness does not significantly affect @-mentions.

Overall models can predict @-mentions across multiple projects.

Abstract

Open Source Software (OSS) project success relies on crowd contributions. When an issue arises in pull-request based systems, @-mentions are used to call on people to task; previous studies have shown that @-mentions in discussions are associated with faster issue resolution. In most projects there may be many developers who could technically handle a variety of tasks. But OSS supports dynamic teams distributed across a wide variety of social and geographic backgrounds, as well as levels of involvement. It is, then, important to know whom to call on, i.e., who can be relied or trusted with important task-related duties, and why. In this paper, we sought to understand which observable socio-technical attributes of developers can be used to build good models of them being future @-mentioned in GitHub issues and pull request discussions. We built overall and project-specific predictive models of future @-mentions, in order to capture the determinants of @-mentions in each of two hundred GitHub projects, and to understand if and how those determinants differ between projects. We found that visibility, expertise, and productivity are associated with an increase in @-mentions, while responsiveness is not, in the presence of a number of control variables. Also, we find that though project-specific differences exist, the overall model can be used for cross-project prediction, indicating its GitHub-wide utility.