Machine learning predicts merge conflicts with high accuracy for safe merges using Git features on 267k scenarios from 744 repositories.
Curating github for engineered software projects,
2 Pith papers cite this work. Polarity classification is still indexing.
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cs.SE 2years
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UNVERDICTED 2representative citing papers
Empirical study of 1,932 GitHub projects finds 16% abandonment rate with 41% survival via new core developers, supported by survey of maintainers on motivations and barriers.
citing papers explorer
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Predicting Merge Conflicts in Collaborative Software Development
Machine learning predicts merge conflicts with high accuracy for safe merges using Git features on 267k scenarios from 744 repositories.
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On the abandonment and survival of open source projects: An empirical investigation
Empirical study of 1,932 GitHub projects finds 16% abandonment rate with 41% survival via new core developers, supported by survey of maintainers on motivations and barriers.