Demo Data from SIGIR 2026 / reviewers in the wild / expert
Yiran Chen 0005
dblp:80/1641-5
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2023
—ORCID · unresolved
Domains — the database's venue-derived domains; a paper can count in several
Software engineering, systems software and programming languages · 1 · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Software engineering, system software, and programming languages
1 paper |
Empirical software engineering · 100% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Empirical software engineering
mining software repositories |
0.7 | 1 | 2023 | Is It Enough to Recommend Tasks to Newcomers? Understanding Mentoring on Good First Issues · ICSE 2023 |
Empirical software engineering › open source software
open source communities |
0.7 | 1 | 2023 | Is It Enough to Recommend Tasks to Newcomers? Understanding Mentoring on Good First Issues · ICSE 2023 |
Empirical software engineering › developer studies › software teams
newcomer onboarding |
0.2 | 1 | 2023 | Is It Enough to Recommend Tasks to Newcomers? Understanding Mentoring on Good First Issues · ICSE 2023 |
Methods — techniques the papers use, named apart from their topics
generalized linear models · 0.7
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2023 | Is It Enough to Recommend Tasks to Newcomers? Understanding Mentoring on Good First IssuesabstractNewcomers are critical for the success and continuity of open source software (OSS) projects. To attract newcomers and facilitate their onboarding, many OSS projects recommend tasks for newcomers, such as good first issues (GFIs). Previous studies have preliminarily investigated the effects of GFIs and techniques to identify suitable GFIs. However, it is still unclear whether just recommending tasks is enough and how significant mentoring is for newcomers. To better understand mentoring in OSS communities, we analyze the resolution process of 48,402 GFIs from 964 repositories through a mix-method approach. We investigate the extent, the mentorship structures, the discussed topics, and the relevance of expert involvement. We find that ~70% of GFIs have expert participation, with each GFI usually having one expert who makes two comments. Half of GFIs will receive their first expert comment within 8.5 hours after a newcomer comment. Through analysis of the collaboration networks of newcomers and experts, we observe that community mentorship presents four types of structure: centralized mentoring, decentralized mentoring, collaborative mentoring, and distributed mentoring. As for discussed topics, we identify 14 newcomer challenges and 18 expert mentoring content. By fitting the generalized linear models, we find that expert involvement positively correlates with newcomers' successful contributions but negatively correlates with newcomers' retention. Our study manifests the status and significance of mentoring in the OSS projects, which provides rich practical implications for optimizing the mentoring process and helping newcomers contribute smoothly and suecessfully, Xin Tan 0003, Yiran Chen 0005, Haohua Wu, Minghui Zhou 0001, Li Zhang 0029 |
ICSE | 2 |