Beyond the Grave: An Empirical Study of Dormancy and Revival in Scientific Open-Source Software

2026-06-18

AI Summary

This study addresses the challenge of distinguishing between permanently abandoned and temporarily dormant scientific open-source software, a limitation of existing methods relying on fixed inactivity thresholds. Through empirical analysis of dormancy causes, revival mechanisms, revival sustainability, and lifecycle patterns, the authors propose a multidimensional discriminative model integrating dormancy duration, lifecycle archetypes, and contributor continuity. A rule-based five-dimensional classifier was constructed using stratified sampling, multi-coder annotation, and a two-stage arbitration protocol (Kappa = 0.779–0.857). Key findings reveal that 52.5% of projects exhibit indeterminate dormancy causes; non-sustained revivals occur 2.14 times more frequently than sustained ones; and 11.5% of apparent “revivals” are artifacts of bot activity or one-off contributions. Among the features examined, lifecycle archetype demonstrates the strongest predictive power for revival sustainability.

Abstract

Background. Inactivity thresholds classify scientific open-source software (OSS) as abandoned but cannot distinguish permanent abandonment from temporary dormancy; moving the cutoff from 1 to 36 months changes the abandoned count in the SciCat corpus from 18,030 to 8,010. Aims. We characterize dormancy causes, revival mechanisms, recovery durability, and lifecycle archetypes in dormant-revived scientific OSS. Method. From 18,247 SciCat repositories we identify 2,984 dormant-revived candidates and field-code a stratified sample of 750 projects with 75 analyst-coders under a two-phase adjudication protocol (post-adjudication kappa 0.779-0.857). A rule-based classifier produces five dimensions: dormancy cause (T1), revival mechanism (T2), nature of revival work (T3), revival sustainability (T4), and lifecycle archetype (T5). Results. Dormancy cause is unresolvable from repository evidence for 52.5% of projects; among resolvable cases, feature/milestone freeze outnumbers research-output completion 5.4:1. Non-sustained recovery outnumbers sustained 2.14:1; 11.5% of apparent revivals are bot-only or single-spike artifacts. Lifecycle archetype is more strongly associated with sustainability than revival mechanism or work type (medium effect on the structurally-independent subset). Conclusions. A fixed inactivity threshold is insufficient to reliably classify scientific OSS abandonment. Gap duration, lifecycle archetype, and contributor continuity together provide more discriminating information than any single threshold.

Problem

• scientific open-source software
• dormancy
• revival
• abandonment classification
• inactivity threshold

Innovation

• dormancy
• revival
• scientific open-source software
• lifecycle archetype
• sustainability