# Automatic Detecting Unethical Behavior in Open-source Software Projects

## TL;DR

This paper presents a comprehensive study of unethical behaviors in open-source software projects, introduces a taxonomy of 15 unethical types, and proposes an automated detection method called Etor with promising results.

## Contribution

It is the first study to classify various unethical behaviors in OSS from stakeholders' perspectives and develop an automated detection approach using ontological and semantic techniques.

## Key findings

- Identified 15 types of unethical behavior in OSS.
- Etor detects 6 types of unethical behavior with 74.8% accuracy.
- Analyzed 195,621 GitHub issues across 1,765 repositories.

## Abstract

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. Based on 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. This shows the feasibility of automated detection of unethical behavior in OSS projects.