A composed pipeline of betweenness cuts, per-edge logistic classifiers, and GitHub-asserted edges resolves ~107M author identities while reducing the largest cluster from 170,431 to under 7,000 and improving gold recall from 0.44 to 0.70.
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4 Pith papers cite this work. Polarity classification is still indexing.
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A scan of 5.8 billion git commits finds 17.59% carry cryptographic signatures, enabling a key-to-identity graph and trust tiers that calibrate heuristic author disambiguation.
Using a 61-feature readability model, LLM code matches or slightly exceeds human code in readability score, shows distinct issue patterns, and prompt design has limited influence.
In the NPM ecosystem, effort is mostly contributed to and demanded from direct dependencies, with three user groups for demand and two for contribution identified via fuzzy c-means clustering, and company affiliation predicted with AUC-ROC of 0.68 using Random Forest.
citing papers explorer
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Scaling Author Identity Disambiguation to the World of Code: A Methodology
A composed pipeline of betweenness cuts, per-edge logistic classifiers, and GitHub-asserted edges resolves ~107M author identities while reducing the largest cluster from 170,431 to under 7,000 and improving gold recall from 0.44 to 0.70.
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Claimed or Attested? A Commit-Signature Dataset and Identity Trust Tiers across the World of Code
A scan of 5.8 billion git commits finds 17.59% carry cryptographic signatures, enabling a key-to-identity graph and trust tiers that calibrate heuristic author disambiguation.
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Characterizing Readability Issue Patterns and the Role of Prompt Design in LLM-Generated Code
Using a 61-feature readability model, LLM code matches or slightly exceeds human code in readability score, shows distinct issue patterns, and prompt design has limited influence.
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Patterns of Effort Contribution and Demand and User Classification based on Participation Patterns in NPM Ecosystem
In the NPM ecosystem, effort is mostly contributed to and demanded from direct dependencies, with three user groups for demand and two for contribution identified via fuzzy c-means clustering, and company affiliation predicted with AUC-ROC of 0.68 using Random Forest.