BurnRiSc: Toward Non-Invasive Burnout Screening in Open Source from Public Repository Signals

2026-09-16

AI Summary

本文提出BurnRiSc框架，通过分析GitHub活动中的14个行为和语言信号来非侵入性地筛查开源贡献者的倦怠风险，以解决开源社区中难以察觉的过劳问题。

Abstract

Burnout is a chronic occupational syndrome, and open source is close to a worst case for it: maintainers absorb unbounded demand with no manager to reallocate work and no organization to notice decline. The cost is not only personal. Burnout precedes withdrawal, and in projects sustained by a handful of maintainers, one departure can break infrastructure that thousands of downstream systems depend on. Yet the field has no way to see it coming: self-report inventories, the only existing measure, miss exactly the contributors most in need of detection and cannot be applied retroactively, so the field cannot even ask how common burnout is or what helps. We present BurnRiSc, a framework that operationalizes the Oldenburg Burnout Inventory's two dimensions, exhaustion and disengagement, as 14 behavioral and linguistic signals computed from GitHub activity and scored against each contributor's own history. The signals aggregate into two weighted dimension scores, with weights learned from labeled cases, and average into a monthly Burnout Risk Score (BRS). In a preliminary evaluation across 68 contributors in ten repositories (ten disclosed burnout cases, twelve comparable-volume collapses, and 46 comparison contributors), sustained BRS elevation precedes 6 of 10 disclosures by 6-15 months, 8 of 10 when adding peak BRS as a second criterion, and 10 of 10 over any prior time frame. We thus present BurnRiSc as evidence that burnout is screenable from public data.

Problem

• Burnout
• Open Source
• Non-Invasive Screening
• Public Repository Signals
• Maintainers

Innovation

• Non-Invasive Burnout Screening
• Public Repository Signals
• Oldenburg Burnout Inventory
• Behavioral and Linguistic Signals
• Monthly Burnout Risk Score