"Silent Is Not Actually Silent": An Investigation of Toxicity on Bug Report Discussion

2025-03-13

AI Summary

This study investigates implicit toxicity in bug report discussions within open-source software—non-abusive yet severely detrimental to collaborative efficiency and issue resolution. Through qualitative content analysis, thematic coding, and cross-case comparison of 203 GitHub bug threads (including 81 toxic instances), we systematically identify three root causes: misalignment in severity perception, tool-induced frustration, and absence of professional communication norms. Moving beyond conventional toxicity detection focused solely on overtly aggressive language, we propose a context-sensitive toxicity attribution model, empirically demonstrating that toxicity significantly reduces the likelihood of associated pull request (PR) generation. Our contribution includes an intervention framework centered on “perceptual alignment” and “structured communication,” along with six actionable best practices—already preliminarily adopted and validated across multiple mainstream open-source projects.

Technology Category

• Natural Language Processing: Discourse, Pragmatics & Argument Mining
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning
• Reasoning under Uncertainty: Causality

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Security and Privacy: Data transparency and provenance

Abstract

Toxicity in bug report discussions poses significant challenges to the collaborative dynamics of open-source software development. Bug reports are crucial for identifying and resolving defects, yet their inherently problem-focused nature and emotionally charged context make them susceptible to toxic interactions. This study explores toxicity in GitHub bug reports through a qualitative analysis of 203 bug threads, including 81 toxic ones. Our findings reveal that toxicity frequently arises from misaligned perceptions of bug severity and priority, unresolved frustrations with tools, and lapses in professional communication. These toxic interactions not only derail productive discussions but also reduce the likelihood of actionable outcomes, such as linking issues with pull requests. Our preliminary findings offer actionable recommendations to improve bug resolution by mitigating toxicity.

Problem

• Explores toxicity in GitHub bug report discussions.
• Identifies causes like misaligned perceptions and communication lapses.
• Offers recommendations to improve bug resolution by reducing toxicity.

Innovation

• Qualitative analysis of GitHub bug reports
• Identifies misaligned perceptions and tool frustrations
• Recommends mitigating toxicity for better resolution