How Automated Tools Are Making Open Source Software Safer

Table of Links

Abstract and 1. Introduction

2. Background and Related Work

3. Study of Unethical Behavior in OSS

   3.1 RQ1: Types of unethical behavior

   3.2 RQ2: Affected software artifacts

4. Methodology

   4.1 Modeling via SWRL rules

   4.2 Automatic detection of unethical behavior

5. Evaluation

6. Discussion and Implications

7. Threats to Validity

8. 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.

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.

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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