Case study of 18,020 Kubernetes PRs shows label-diff congruence is prevalent and stable, with higher congruence linked to fewer review participants among core developers and more among one-time contributors.
German, Igor Steinmacher, and Marco A
2 Pith papers cite this work. Polarity classification is still indexing.
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μEd is a proposed standard API for interoperable education microservices that integrates feedback, assessment, and chatbot automation from existing institutional systems.
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Efficiency for Experts, Visibility for Newcomers: A Case Study of Label-Code Alignment in Kubernetes
Case study of 18,020 Kubernetes PRs shows label-diff congruence is prevalent and stable, with higher congruence linked to fewer review participants among core developers and more among one-time contributors.
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$\mu$Ed API: Towards a Shared API for Education Microservices
μEd is a proposed standard API for interoperable education microservices that integrates feedback, assessment, and chatbot automation from existing institutional systems.