Why Modern Open Source Projects Fail
Not on the map. Why?
Abstract (arXiv)
Open source is experiencing a renaissance period, due to the appearance of modern platforms and workflows for developing and maintaining public code. As a result, developers are creating open source software at speeds never seen before. Consequently, these projects are also facing unprecedented mortality rates. To better understand the reasons for the failure of modern open source projects, this paper describes the results of a survey with the maintainers of 104 popular GitHub systems that have been deprecated. We provide a set of nine reasons for the failure of these open source projects. We also show that some maintenance practices -- specifically the adoption of contributing guidelines and continuous integration -- have an important association with a project failure or success. Finally, we discuss and reveal the principal strategies developers have tried to overcome the failure of the studied projects.
Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). First posted on arXiv 7 Jul 2017.
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Citing papers per year: 2022: 1; 2023: 0; 2024: 2; 2025: 1; 2026: 4.
8 papers.
- 2024Public-private funding models in open source software development: A case study on scikit-learncited by 5
- 2024An Empirical Study of Challenges in Machine Learning Asset Managementcited by 4
- 2022Automatically Categorising GitHub Repositories by Application Domaincited by 2
- 2026RepoRescue: An Empirical Study of LLM Agents on Whole-Repository Compatibility Rescuecited by 1
- 2025Which Is Better For Reducing Outdated and Vulnerable Dependencies: Pinning or Floating?cited by 0
- 2026Knowledge Lever Risk Management for Software Engineering: A Stochastic Framework for Mitigating Knowledge Losscited by 0
- 2026Decoupling Code Complexity from Newcomer Participation: A Causal Study of AI Coding Agent Adoption in OSScited by 0
- 2026Making Cross-Continental Federated Learning Repeatable with FLIP: a Multi-Application Studycited by 0
- 2026Making Cross-Continental Federated Learning Repeatable with FLIP: a Multi-Application Study28 Sep 2026
- 2026Decoupling Code Complexity from Newcomer Participation: A Causal Study of AI Coding Agent Adoption in OSS2 Jul 2026
- 2026RepoRescue: An Empirical Study of LLM Agents on Whole-Repository Compatibility Rescue1 Jul 2026
- 2026Knowledge Lever Risk Management for Software Engineering: A Stochastic Framework for Mitigating Knowledge Loss25 Apr 2026
- 2025Which Is Better For Reducing Outdated and Vulnerable Dependencies: Pinning or Floating?7 Oct 2025
- 2024Public-private funding models in open source software development: A case study on scikit-learn9 Apr 2024
- 2024An Empirical Study of Challenges in Machine Learning Asset Management25 Feb 2024
- 2022Automatically Categorising GitHub Repositories by Application Domain30 Jul 2022