一句话：提出 PFIRec，基于新手特征与 LambdaMART 为新进入开源项目者推荐个性化首个 issue。
开源项目用 good first issue（GFI）吸引新手，但现有推荐器只给通用 GFI，忽视新手背景差异，导致尝试失败与 onboarding 延迟。本文先分析 37 名新手及其在多个项目解决的首个 issue，发现同一新手偏好的任务类型、编程语言和项目领域相似。进一步识别出专业偏好、开源经验、活跃度与情绪等影响个性化选择的首个 issue 特征。据此提出 PFIRec，用 LambdaMART 对候选 issue 排序。在 100 个 GitHub 项目的 68,858 个 issue 上评估，PFIRec 优于现有推荐器：中位数上使 top 推荐适合特定新手的概率翻倍，并减少约三分之一失败尝试。
Many open source projects provide good first issues (GFIs) to attract and retain newcomers. Although several automated GFI recommenders have been proposed, existing recommenders are limited to recommending generic GFIs without considering differences between individual newcomers. However, we observe mismatches between generic GFIs and the diverse background of newcomers, resulting in failed attempts, discouraged onboarding, and delayed issue resolution. To address this problem, we assume that personalized first issues (PFIs) for newcomers could help reduce the mismatches. To justify the assumption, we empirically analyze 37 newcomers and their first issues resolved across multiple projects. We find that the first issues resolved by the same newcomer share similarities in task type, programming language, and project domain. These findings underscore the need for a PFI recommender to improve over state-of-the-art approaches. For that purpose, we identify features that influence newcomers' personalized selection of first issues by analyzing the relationship between possible features of the newcomers and the characteristics of the newcomers' chosen first issues. We find that the expertise preference, OSS experience, activeness, and sentiment of newcomers drive their personalized choice of the first issues. Based on these findings, we propose a Personalized First Issue Recommender (PFIRec), which employs LamdaMART to rank candidate issues for a given newcomer by leveraging the identified influential features. We evaluate PFIRec using a dataset of 68,858 issues from 100 GitHub projects. The evaluation results show that PFIRec outperforms existing first issue recommenders, potentially doubling the probability that the top recommended issue is suitable for a specific newcomer and reducing one-third of a newcomer's unsuccessful attempts to identify suitable first issues, in the median. We provide a replication package at https://zenodo.org/record/7915841.
DOI 原文 · arXiv · PDF（开放获取） · DBLP
@inproceedings{abs-2308-09038,
title = {Personalized First Issue Recommender for Newcomers in Open Source Projects},
author = {Wenxin Xiao and Jingyue Li and Hao He and Ruiqiao Qiu and Minghui Zhou},
booktitle = {ASE},
year = {2023},
doi = {10.1109/ase56229.2023.00158}
}