Fork-based development enables parallel evolution of software, but unsynchronized contributions create persistent divergence: security patches, bug fixes, and quality improvements often fail to propagate across fork families, leaving downstream users exposed to known vulnerabilities or bugs and missing massive opportunities to improve the other repositories in the family. We present the first large-scale empirical study of fork synchronization, analyzing popular GitHub fork families with 3,820 actively maintained forks, and developed a monitoring platform to mine the valuable commits and promote their swift merging. Our findings reveal a synchronization paradox: while 90% of submitted pull requests are merged, only 6.92% of fork commits ever appear in PRs, leaving massive fork development permanently unsynchronized across the families. Synchronization delay is pervasive and structurally uneven where fork propagation accounts for 72.9% of end-to-end commit lifecycle delay. Contrary to common assumptions, PR rejection is rarely caused by technical incorrectness; instead, 65% of rejections stem from superseded contributions, process violations, or maintainer policy decisions. Based on these insights, we develop a three-stage syncability assessment pipeline that identifies fork-local commits that are both sync-worthy (broadly beneficial) and sync-eligible (technically and policy-compatibly portable). Applied to 0.5 million fork-local commits, our pipeline surfaces 12,284 sync-ready commit--repository pairs, demonstrating that our approach identifies practically valuable changes. To further validate the security impact, we manually reviewed 153 security-related commit--repository pairs and confirmed 83 as potential 1-day vulnerabilities, for which we produced 35 proof-of-concept of exploit demonstrations and filed issues to the affected repositories. Our monitoring platform enables continuous, near-real-time detection of synchronization opportunities across fork families, improving the sustainability of fork-based open-source ecosystems.
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Zhu et al. (2026) studied this question.
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