Topic-based Integrator Matching for Pull Request
This addresses efficiency in open-source software development by automating PR-integrator matching, but it appears incremental as it builds on existing topic-based methods.
The authors tackled the problem of manually matching pull requests (PRs) to integrators in GitHub by proposing a Topic-based Integrator Matching Algorithm (TIMA), which predicts relevant collaborators based on textual semantics and topic-relation matrices, though no concrete performance numbers are provided.
Pull Request (PR) is the main method for code contributions from the external contributors in GitHub. PR review is an essential part of open source software developments to maintain the quality of software. Matching a new PR for an appropriate integrator will make the PR reviewing more effective. However, PR and integrator matching are now organized manually in GitHub. To make this process more efficient, we propose a Topic-based Integrator Matching Algorithm (TIMA) to predict highly relevant collaborators(the core developers) as the integrator to incoming PRs . TIMA takes full advantage of the textual semantics of PRs. To define the relationships between topics and collaborators, TIMA builds a relation matrix about topic and collaborators. According to the relevance between topics and collaborators, TIMA matches the suitable collaborators as the PR integrator.