Open Access

ARTICLE

Code Reviewer Intelligent Prediction in Open Source Industrial Software Project

Zhifang Liao¹, Bolin Zhang¹, Xuechun Huang¹, Song Yu^1,*, Yan Zhang²

1 School of Computer Science and Engineering, Central South University, Changsha, 410083, China
2 Department of Computing, School of Computing, Engineering and Built Environment, Glasgow Caledonian University, Glasgow, G4 0BA, UK

* Corresponding Author: Song Yu. Email:

(This article belongs to the Special Issue: Machine Learning-Guided Intelligent Modeling with Its Industrial Applications)

Computer Modeling in Engineering & Sciences 2023, 137(1), 687-704. https://doi.org/10.32604/cmes.2023.027466

Received 31 October 2022; Accepted 22 December 2022; Issue published 23 April 2023

Abstract

Currently, open-source software is gradually being integrated into industrial software, while industry protocols in industrial software are also gradually transferred to open-source community development. Industrial protocol standardization organizations are confronted with fragmented and numerous code PR (Pull Request) and informal proposals, and different workflows will lead to increased operating costs. The open-source community maintenance team needs software that is more intelligent to guide the identification and classification of these issues. To solve the above problems, this paper proposes a PR review prediction model based on multi-dimensional features. We extract 43 features of PR and divide them into five dimensions: contributor, reviewer, software project, PR, and social network of developers. The model integrates the above five-dimensional features, and a prediction model is built based on a Random Forest Classifier to predict the review results of PR. On the other hand, to improve the quality of rejected PRs, we focus on problems raised in the review process and review comments of similar PRs. We propose a PR revision recommendation model based on the PR review knowledge graph. Entity information and relationships between entities are extracted from text and code information of PRs, historical review comments, and related issues. PR revisions will be recommended to code contributors by graph-based similarity calculation. The experimental results illustrate that the above two models are effective and robust in PR review result prediction and PR revision recommendation.

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Keywords

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