The authors propose DCN and PDCN, new GNN architectures using causal graph filters for convolutional learning on DAGs, with established equivariance properties and competitive empirical performance.
A survey of machine learning for big code and naturalness
2 Pith papers cite this work. Polarity classification is still indexing.
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2025 2verdicts
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Large-scale study of GitHub AI code review actions finds concise comments with code snippets, manual triggers, and hunk-level tools are more likely to produce code changes.
citing papers explorer
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Directed Acyclic Graph Convolutional Networks
The authors propose DCN and PDCN, new GNN architectures using causal graph filters for convolutional learning on DAGs, with established equivariance properties and competitive empirical performance.
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Does AI Code Review Lead to Code Changes? A Case Study of GitHub Actions
Large-scale study of GitHub AI code review actions finds concise comments with code snippets, manual triggers, and hunk-level tools are more likely to produce code changes.