A Novel Approach for Estimating Truck Factors
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Abstract (arXiv)
Truck Factor (TF) is a metric proposed by the agile community as a tool to identify concentration of knowledge in software development environments. It states the minimal number of developers that have to be hit by a truck (or quit) before a project is incapacitated. In other words, TF helps to measure how prepared is a project to deal with developer turnover. Despite its clear relevance, few studies explore this metric. Altogether there is no consensus about how to calculate it, and no supporting evidence backing estimates for systems in the wild. To mitigate both issues, we propose a novel (and automated) approach for estimating TF-values, which we execute against a corpus of 133 popular project in GitHub. We later survey developers as a means to assess the reliability of our results. Among others, we find that the majority of our target systems (65%) have TF <= 2. Surveying developers from 67 target systems provides confidence towards our estimates; in 84% of the valid answers we collect, developers agree or partially agree that the TF's authors are the main authors of their systems; in 53% we receive a positive or partially positive answer regarding our estimated truck factors.
Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). First posted on arXiv 22 Apr 2016.
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Citing papers per year: 2024: 1; 2025: 0; 2026: 5.
6 papers.
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- 2026Knowledge Lever Risk Management for Software Engineering: A Stochastic Framework for Mitigating Knowledge Losscited by 0
- 2026The Substrate Collapse: AI Code Generation Invalidates Authorship-Based Knowledge Metricscited by 0
- 2026BurnRiSc: Toward Non-Invasive Burnout Screening in Open Source from Public Repository Signalscited by 0
- 2026BurnRiSc: Toward Non-Invasive Burnout Screening in Open Source from Public Repository Signals16 Sep 2026
- 2026The Substrate Collapse: AI Code Generation Invalidates Authorship-Based Knowledge Metrics18 Jun 2026
- 2026Knowledge Lever Risk Management for Software Engineering: A Stochastic Framework for Mitigating Knowledge Loss25 Apr 2026
- 2026The CriticalSet problem: Identifying Critical Contributors in Bipartite Dependency Networks23 Apr 2026
- 2026Project resilience as network robustness19 Apr 2026
- 2024Leveraging Reviewer Experience in Code Review Comment Generation17 Sep 2024