Early Prediction of GitHub Issue Escalation Using Text and Collaboration Features

Abstract

Problem: GitHub issues can develop into prolonged, contentious, or coordination-intensive episodes, but escalation is usually recognized only after substantial maintainer effort has been spent.

Motivation: An early warning could help maintainers prioritize attention while preserving human judgment.

Gap: Existing work largely predicts assignment, severity, priority, or resolution time; these targets do not jointly represent escalation, and many approaches underuse the interaction process visible at the beginning of an issue.

Framework: This paper proposes a leakage-aware conceptual framework that predicts escalation at fixed early cutoffs, including issue creation, the first maintainer response, and the first few comments. Observable future events—such as reopening, priority promotion, cross-repository transfer or reference, senior-contributor assignment, abnormal unresolved duration, discussion growth, and validated conflict—define the outcome but are excluded from predictors. Early predictors combine title, body, and comment representations with participant growth, response timing, interaction networks, maintainer involvement, and repository controls. Interpretable linear and tree models and multimodal neural fusion are specified, together with calibrated, alert-budget-aware evaluation.

Contributions: The proposal provides an operational escalation construct, a temporal feature taxonomy, a reproducible multi-repository methodology, and an explanation layer for maintainer-facing alerts. No empirical performance claim is made; the expected contribution is a testable design for future evaluation and responsible deployment.