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P2 Developer Reputation Estimator (DRE)

开发者声誉估计器(DRE)
ASE 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE)2019匹配度 1/10 · 该论文聚焦开源开发者声誉估计与 Git 身份合并,属于软件挖掘/开发者工具方向,与用户关注的静态分析、LLM/Agent 程序分析、浮点程序分析及贝叶斯程序分析均无直接关联。引用 14 inbox
Sadika Amreen, Andrey Karnauch, Audris Mockus
crossref: 2026-09-12

AI 速览

一句话:提出基于 World of Code 的开发者声誉估计器,通过聚合 Git 数据、合并身份并度量工作影响来生成开发者档案。

在自由/开源软件中,开发者分散全球且常不相识,声誉对接受 pull request 或解决问题至关重要。现有方案多局限于 GitHub、侧重活动而非影响、未利用社交或技术网络,且不纠正错误拼写的开发者身份。本文提出开发者声誉估计器 DRE,基于 World of Code 聚合几乎所有公共 Git 仓库数据;开发者可先选择属于本人的 3400 万个 Git commit author ID,系统将这些 ID 视为同一人并生成档案,进而度量其工作影响、展示合作者并纠正身份数据。身份选择还作为监督学习训练集,用于合并同一人的多个身份字符串。作者计划评估工具与所提影响度量,并基于结果开发可与 pull request、commit 关联的声誉徽章,帮助开发者信任和排序贡献。

问题
自由/开源软件中开发者声誉对接受 pull request 和解决问题很重要,但现有方案多为特定 forge、偏重活动而非影响、不利用网络且不处理身份拼写错误。
方法
基于 World of Code 聚合公共 Git 仓库,让开发者选择属于本人的 commit author ID 以生成档案,并用监督学习合并多个身份字符串,同时度量工作影响和合作者。
评估
作者计划评估该工具及提出的影响度量;摘要未报告具体实验指标,并期望据此开发可关联 pull request/commit 的声誉徽章。
原文摘要(Abstract)

Evidence shows that developer reputation is extremely important when accepting pull requests or resolving reported issues. It is particularly salient in Free/Libre Open Source Software since the developers are distributed around the world, do not work for the same organization and, in most cases, never meet face to face. The existing solutions to expose developer reputation tend to be forge specific (GitHub), focus on activity instead of impact, do not leverage social or technical networks, and do not correct often misspelled developer identities. We aim to remedy this by amalgamating data from all public Git repositories, measuring the impact of developer work, expose developer's collaborators, and correct notoriously problematic developer identity data. We leverage World of Code (WoC), a collection of an almost complete (and continuously updated) set of Git repositories by first allowing developers to select which of the 34 million(M) Git commit author IDs belong to them and then generating their profiles by treating the selected collection of IDs as that single developer. As a side-effect, these selections serve as a training set for a supervised learning algorithm that merges multiple identity strings belonging to a single individual. As we evaluate the tool and the proposed impact measure, we expect to build on these findings to develop reputation badges that could be associated with pull requests and commits so developers could easier trust and prioritize them.

链接与引用

DOI 原文 ·

BibTeX
@inproceedings{paperbot5269,
  title = {Developer Reputation Estimator (DRE)},
  author = {Sadika Amreen and Andrey Karnauch and Audris Mockus},
  booktitle = {ASE},
  year = {2019},
  doi = {10.1109/ase.2019.00107}
}