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Defect Detection On Codexglue Devign

Métriques

Accuracy

Résultats

Résultats de performance de divers modèles sur ce benchmark

Nom du modèle	Accuracy	Paper Title	Repository
PLBART + GFSA	62.96	Graph Convolutions Enrich the Self-Attention in Transformers!	-
CodeT5-small + GFSA	63.69	Graph Convolutions Enrich the Self-Attention in Transformers!	-
CodeT5-small	63.25	Graph Convolutions Enrich the Self-Attention in Transformers!	-
RoBERTa + GFSA	64.39	Graph Convolutions Enrich the Self-Attention in Transformers!	-
PLBART	62.63	Graph Convolutions Enrich the Self-Attention in Transformers!	-
CodeT5	65.78	CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation	-
CodeT5-base	63.51	Graph Convolutions Enrich the Self-Attention in Transformers!	-
CodeBERT + GFSA	64.49	Graph Convolutions Enrich the Self-Attention in Transformers!	-
RoBERTa	62.88	Graph Convolutions Enrich the Self-Attention in Transformers!	-
CodeBERT	64.31	Graph Convolutions Enrich the Self-Attention in Transformers!	-
CodeBERT	62.08	CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation	-
CodeT5-base + GFSA	64.75	Graph Convolutions Enrich the Self-Attention in Transformers!	-

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Defect Detection On Codexglue Devign | SOTA | HyperAI