Towards Automated Detection of Unethical Behavior in Open-Source Software Projects

Win, Hsu Myat1; Wang, Haibo1; Tan, Shin Hwei2

10.1145/3611643.3616314

2023

31st ACM Joint Meeting of the European Software Engineering Conference / Symposium on the Foundations-of-Software-Engineering (ESEC/FSE)

PROCEEDINGS OF THE 31ST ACM JOINT MEETING EUROPEAN SOFTWARE ENGINEERING CONFERENCE AND SYMPOSIUM ON THE FOUNDATIONS OF SOFTWARE ENGINEERING, ESEC/FSE 2023

DEC 03-09, 2023

null,San Francisco,CA

1601 Broadway, 10th Floor, NEW YORK, NY, UNITED STATES

ASSOC COMPUTING MACHINERY

Given the rapid growth of Open-Source Software (OSS) projects, ethical considerations are becoming more important. Past studies focused on specific ethical issues (e.g., gender bias and fairness in OSS). There is little to no study on the different types of unethical behavior in OSS projects. We present the first study of unethical behavior in OSS projects from the stakeholders' perspective. Our study of 316 GitHub issues provides a taxonomy of 15 types of unethical behavior guided by six ethical principles (e.g., autonomy). Examples of new unethical behavior include soft forking (copying a repository without forking) and self-promotion (promoting a repository without self-identifying as contributor to the repository). We also identify 18 types of software artifacts affected by the unethical behavior. The diverse types of unethical behavior identified in our study (1) call for attentions of developers and researchers when making contributions in GitHub, and (2) point to future research on automated detection of unethical behavior in OSS projects. From our study, we propose Etor, an approach that can automatically detect six types of unethical behavior by using ontological engineering and Semantic Web Rule Language (SWRL) rules to model GitHub attributes and software artifacts. Our evaluation on 195,621 GitHub issues (1,765 GitHub repositories) shows that Etor can automatically detect 548 unethical behavior with 74.8% average true positive rate (up to 100% true positive rate). This shows the feasibility of automated detection of unethical behavior in OSS projects.

Ethics in Software Engineering Open-source software projects

第一

英语

EI ; CPCI-S

Computer Science

Computer Science, Software Engineering ; Computer Science, Theory & Methods

WOS:001148157800053

Web of Science

1.Southern University of Science and Technology, China
2.Concordia University, Canada

Win, Hsu Myat,Wang, Haibo,Tan, Shin Hwei. Towards Automated Detection of Unethical Behavior in Open-Source Software Projects[C]. 1601 Broadway, 10th Floor, NEW YORK, NY, UNITED STATES:ASSOC COMPUTING MACHINERY,2023.