Extension Decisions in Open Source Software Ecosystem
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866916872535932928 |
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| author | Onagh, Elmira Nayebi, Maleknaz |
| author_facet | Onagh, Elmira Nayebi, Maleknaz |
| contents | GitHub Marketplace is expanding by approximately 41% annually, with new tools; however, many additions replicate existing functionality. We study this phenomenon in the platform's largest segment, Continuous Integration (CI), by linking 6,983 CI Actions to 3,869 providers and mining their version histories. Our graph model timestamps every functionality's debut, tracks its adoption, and clusters redundant tools. We find that approximately 65% of new CI Actions replicate existing capabilities, typically within six months, and that a small set of first-mover Actions accounts for most subsequent forks and extensions. These insights enable developers to choose the optimal moment to launch, target unmet functionality, and help maintainers eliminate redundant tools. We publish the complete graph and dataset to encourage longitudinal research on innovation and competition in software ecosystems, and to provide practitioners with a data-driven roadmap for identifying emerging trends and guiding product strategy. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_23168 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Extension Decisions in Open Source Software Ecosystem Onagh, Elmira Nayebi, Maleknaz Software Engineering GitHub Marketplace is expanding by approximately 41% annually, with new tools; however, many additions replicate existing functionality. We study this phenomenon in the platform's largest segment, Continuous Integration (CI), by linking 6,983 CI Actions to 3,869 providers and mining their version histories. Our graph model timestamps every functionality's debut, tracks its adoption, and clusters redundant tools. We find that approximately 65% of new CI Actions replicate existing capabilities, typically within six months, and that a small set of first-mover Actions accounts for most subsequent forks and extensions. These insights enable developers to choose the optimal moment to launch, target unmet functionality, and help maintainers eliminate redundant tools. We publish the complete graph and dataset to encourage longitudinal research on innovation and competition in software ecosystems, and to provide practitioners with a data-driven roadmap for identifying emerging trends and guiding product strategy. |
| title | Extension Decisions in Open Source Software Ecosystem |
| topic | Software Engineering |
| url | https://arxiv.org/abs/2507.23168 |