Topic-based Integrator Matching for Pull Request

TL;DR

This paper introduces TIMA, a topic-based algorithm that automates the matching of pull requests to the most relevant integrators by leveraging textual semantics, improving efficiency in open source project review processes.

Contribution

The paper presents a novel topic-based algorithm that automates PR-integrator matching using semantic analysis, reducing manual effort and increasing accuracy.

Findings

TIMA effectively predicts relevant integrators for PRs.

The approach improves review efficiency in open source projects.

Semantic analysis enhances collaborator matching accuracy.

Abstract

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.