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CodeBERT: A Pre-Trained Model for Programming and Natural Languages

Zhangyin Feng, Daya Guo, Duyu Tang, Nan Duan, Xiaocheng Feng, Ming Gong, Linjun Shou, Bing Qin, Ting Liu, Daxin Jiang, Ming Zhou

Findings of the Association for Computational Linguistics: EMNLP 2020, 2020. DOI 10.18653/v1/2020.findings-emnlp.139

Has this study been replicated?

The atlas records 1 replication of this study. Recorded outcomes: 1 mixed.

Replications

• CodeBERT for Code Clone Detection: A Replication Study, Arshad et al. (2022). Outcome recorded: mixed. CodeBERT performs the best for detecting Type-1 and Type-4 clones with a 100% and 96% recall on average respectively. We also find that there is limited generalizability on unseen functionalities where recall drops by 15% and 40% on the SemanticCloneBench and Android datasets respectively. View paper

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