CriticalSet identifies the k contributors whose removal isolates the largest number of items in a bipartite dependency network, solved via ShapleyCov centrality derived from the Shapley value and the linear-time MinCov peeling algorithm.
InProceedings of the 44th International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP ’22)
4 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 4representative citing papers
Modeling projects as networks provides more consistent estimates of resilience to key personnel loss than existing methods.
OSS4SG projects retain contributors at 2.2X higher rates with 19.6% higher core status probability than conventional OSS, and a late-spike temporal pattern enables faster core achievement (21 weeks) than early intensive contributions.
CodeQ aggregates token rationales into code categories to enable global interpretability of LLMs, claiming over 50% entropy reduction and revealing model preference for syntactic cues plus human misalignment in a 37-person study.
citing papers explorer
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The CriticalSet problem: Identifying Critical Contributors in Bipartite Dependency Networks
CriticalSet identifies the k contributors whose removal isolates the largest number of items in a bipartite dependency network, solved via ShapleyCov centrality derived from the Shapley value and the linear-time MinCov peeling algorithm.
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Project resilience as network robustness
Modeling projects as networks provides more consistent estimates of resilience to key personnel loss than existing methods.
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Do Good, Stay Longer? Temporal Patterns and Predictors of Newcomer-to-Core Transitions in Conventional OSS and OSS4SG
OSS4SG projects retain contributors at 2.2X higher rates with 19.6% higher core status probability than conventional OSS, and a late-spike temporal pattern enables faster core achievement (21 weeks) than early intensive contributions.
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Enabling Global, Human-Centered Explanations for LLMs:From Tokens to Interpretable Code and Test Generation
CodeQ aggregates token rationales into code categories to enable global interpretability of LLMs, claiming over 50% entropy reduction and revealing model preference for syntactic cues plus human misalignment in a 37-person study.