A ground-truth dataset and classification model for detecting bots in GitHub issue and PR comments-Reference-Cited by-同舟云学术

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A ground-truth dataset and classification model for detecting bots in GitHub issue and PR comments

Published:2021-05 Issue: Volume:175 Page:110911
ISSN:0164-1212
Container-title:Journal of Systems and Software
language:en
Short-container-title:Journal of Systems and Software

Author:

Golzadeh Mehdi^ORCID,Decan Alexandre^ORCID,Legay Damien^ORCID,Mens Tom^ORCID

Funder

Fonds De La Recherche Scientifique - FNRS

Publisher

Elsevier BV

Subject

Hardware and Architecture,Information Systems,Software

Reference74 articles.

1. Dissecting a social Botnet: Growth, content and influence in Twitter;Abokhodair,2015

2. Instance-based learning algorithms;Aha;Mach. Learn.,1991

3. CATS: Characterizing automation of Twitter spammers;Amleshwaram,2013

4. Reducing human effort and improving quality in peer code reviews using automatic static analysis and reviewer recommendation;Balachandran,2013

5. Animated conversational agents and e-consumer productivity: The roles of agents and individual characteristics;Ben Mimoun;Inf. Manag.,2017

Cited by 46 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Automatic Core-Developer Identification on GitHub: A Validation Study;ACM Transactions on Software Engineering and Methodology;2023-09-30

2. GitHub Actions: The Impact on the Pull Request Process;Empirical Software Engineering;2023-09-26

3. From Commit Message Generation to History-Aware Commit Message Completion;2023 38th IEEE/ACM International Conference on Automated Software Engineering (ASE);2023-09-11

4. More than React: Investigating the Role of Emoji Reaction in GitHub Pull Requests;Empirical Software Engineering;2023-09

5. BDGOA: A bot detection approach for GitHub OAuth Apps;Intelligent and Converged Networks;2023-09

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