Table of Links Abstract and 1. Introduction Abstract and 1. Introduction Background and Related Work


Study of Unethical Behavior in OSS
3.1 RQ1: Types of unethical behavior
3.2 RQ2: Affected software artifacts


Methodology
4.1 Modeling via SWRL rules
4.2 Automatic detection of unethical behavior


Evaluation


Discussion and Implications


Threats to Validity


Conclusion and References Background and Related Work Background and Related Work Background and Related Work Study of Unethical Behavior in OSS
3.1 RQ1: Types of unethical behavior
3.2 RQ2: Affected software artifacts Study of Unethical Behavior in OSS Study of Unethical Behavior in OSS 3.1 RQ1: Types of unethical behavior 3.1 RQ1: Types of unethical behavior 3.2 RQ2: Affected software artifacts 3.2 RQ2: Affected software artifacts Methodology
4.1 Modeling via SWRL rules
4.2 Automatic detection of unethical behavior Methodology Methodology 4.1 Modeling via SWRL rules 4.1 Modeling via SWRL rules 4.2 Automatic detection of unethical behavior 4.2 Automatic detection of unethical behavior Evaluation Evaluation Evaluation Discussion and Implications Discussion and Implications Discussion and Implications Threats to Validity Threats to Validity Threats to Validity Conclusion and References Conclusion and References Conclusion and References 7 THREATS TO VALIDITY External. Our findings of unethical behavior may not generalize beyond the studied OSS projects and issues/PRs. There could be unethical behavior that are not reported to the issue tracker. Unfortunately, there is no conceivable way to study these unreported issues. As some issues may not have the ethics-related keywords that we used for searching, we could have also missed some unethical behavior. Nevertheless, our selected keywords already help us in discovering many types of unethical behavior. Hence, we believe the issues in our study provide a representative sample of the reported and resolved unethical issues in our studied repositories. While other types of unethical behavior discovered in our study is important, Etor can only detect six of them, and our evaluation is limited to these six types. Nevertheless, our experiments show that Etor can detect unethical behavior with relatively high accuracy. External Internal. Our code and scripts may have bugs that can affect our results. To mitigate this threat, we make our tool and data publicly available for inspection. Internal 8 CONCLUSION To better understand unethical behavior in OSS projects, we conduct a study of the types of unethical behavior in OSS projects. By reading and analyzing the discussion of stakeholders in OSS projects, our study of 316 GitHub issues identifies 15 types of unethical behavior. These unethical behaviors are affected by various types of software artifacts. Inspired by our study, we propose Etor, an ontology-based approach that can automatically detect unethical behavior. Our evaluation of Etor on 195,621 issues (1,765 repositories) shows that Etor can automatically detect 548 issues with 74.8% TP rate on average. As the first study that investigates the types of unethical behavior in OSS projects, we hope to raise awareness among OSS stakeholders regarding the importance of understanding ethical issues in OSS projects. While Etor shows promising results in automated detection of unethical behavior in OSS projects, we plan to enhance Etor in future to detect more types and reduce false positives using machine learning techniques. 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(1) Hsu Myat Win, Southern University of Science and Technology, China (11960003@mail.sustech.edu.cn);
(2) Haibo Wang, Southern University of Science and Technology, China (wanghb2020@mail.sustech.edu.cn);
(3) Shin Hwei Tan, a corresponding author from Southern University of Science and Technology, China (tansh3@sustech.edu.cn). Authors: Authors: (1) Hsu Myat Win, Southern University of Science and Technology, China (11960003@mail.sustech.edu.cn); (2) Haibo Wang, Southern University of Science and Technology, China (wanghb2020@mail.sustech.edu.cn); (3) Shin Hwei Tan, a corresponding author from Southern University of Science and Technology, China (tansh3@sustech.edu.cn). This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv available on arxiv

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