一句话：提出 GPP，用多图表示与图神经网络利用代码评审请求的属性及关系，输出优先级排序列表。
代码评审请求（CRR）在大型活跃项目中容易积压，阻碍 CI/CD，因此需优先处理相关 CRR。既有研究表明 CRR 的属性及其与其他 CRR 的关系均影响评审优先级，但现有排序器很少利用关系。本文提出 GPP：用多图结构表示一组 CRR，以图神经网络学习排序适配表示，并输出有序 CRR 列表。评估将相关 CRR 定义为在 CI/CD 中短时、少迭代即被合并的请求，在 15 个开源项目、超 42 万条 CRR 上与 2 个规则和 6 个学习排序器比较。GPP 在 NDCG（82.94%）、MRR（36.52%）、MAP（63.80%）上均优于基线，并有助于推荐最相关 CRR 和平衡多目标。
Peer code review has become a must-have in modern software development. However, many code review requests (CRRs) could be a backlog for large-scale and active projects, blocking continuous integration and continuous delivery (CI/CD). Prioritizing CRRs to make the relevant ones to be reviewed first is a critical method for addressing this issue. Early studies have shown that many factors affect the review priority of a CRR, including its properties and relationships with other CRRs. However, the relationships, e.g., modifying the same files and sharing the same authors, are rarely considered when developing CRR prioritizers. In this paper, we propose a Graph-Powered Prioritizer (namely GPP) to make full use of the properties and relationships of CRRs. GPP uses the multi-graph structure to develop an initial representation of a collection of CRRs and uses the graph neural network algorithm to learn the prioritization-adapted representation, and eventually, outputs an ordered list of CRRs based on it. With experimental evaluation, we define relevant CRRs in the context of CI/CD as those that are likely to achieve three objectives, i.e., being merged while undergoing a few iterations in a short duration. We compare GPP against two rule-based and six learning-based prioritizers on 15 open-source software projects with more than 420K CRRs. The experimental results indicate that GPP outperforms the baselines on three basic ranking-aware evaluation metrics, including NDCG (82.94%), MRR (36.52%), and MAP (63.80%); while providing benefits in recommending the most relevant CRRs and balancing multiple objectives. Data&materials: https://figshare.com/s/133f23da558b7b254041
DOI 原文 ·
@inproceedings{paperbot8411,
title = {GPP: A Graph-Powered Prioritizer for Code Review Requests},
author = {Lanxin Yang and Jinwei Xu and He Zhang and Fanghao Wu and Jun Lyu and Yue Li and Alberto Bacchelli},
booktitle = {ASE},
year = {2024},
doi = {10.1145/3691620.3694990}
}