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Defect Detection On Codexglue Devign

Metrics

Accuracy

Results

Performance results of various models on this benchmark

Model Name
Accuracy
Paper TitleRepository
PLBART + GFSA62.96Graph Convolutions Enrich the Self-Attention in Transformers!
CodeT5-small + GFSA63.69Graph Convolutions Enrich the Self-Attention in Transformers!
CodeT5-small63.25Graph Convolutions Enrich the Self-Attention in Transformers!
RoBERTa + GFSA64.39Graph Convolutions Enrich the Self-Attention in Transformers!
PLBART62.63Graph Convolutions Enrich the Self-Attention in Transformers!
CodeT565.78CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation
CodeT5-base63.51Graph Convolutions Enrich the Self-Attention in Transformers!
CodeBERT + GFSA64.49Graph Convolutions Enrich the Self-Attention in Transformers!
RoBERTa62.88Graph Convolutions Enrich the Self-Attention in Transformers!
CodeBERT64.31Graph Convolutions Enrich the Self-Attention in Transformers!
CodeBERT62.08CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation
CodeT5-base + GFSA64.75Graph Convolutions Enrich the Self-Attention in Transformers!
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