Deep Learning Meets Software Engineering: A Survey on Pre-Trained Models of Source Code

24 May 2022

Papers citing "Deep Learning Meets Software Engineering: A Survey on Pre-Trained Models of Source Code"

9 / 9 papers shown

Title
Natural Language Generation and Understanding of Big Code for AI-Assisted Programming: A Review M. Wong Shangxin Guo Ching Nam Hang Siu-Wai Ho C. Tan 25 77 0 04 Jul 2023
The EarlyBIRD Catches the Bug: On Exploiting Early Layers of Encoder Models for More Efficient Code Classification Anastasiia Grishina Max Hort Leon Moonen 19 6 0 08 May 2023
CrossCodeBench: Benchmarking Cross-Task Generalization of Source Code Models Changan Niu Chuanyi Li Vincent Ng Bin Luo ELM ALM 19 9 0 08 Feb 2023
A Survey on Natural Language Processing for Programming Qingfu Zhu Xianzhen Luo Fang Liu Cuiyun Gao Wanxiang Che 13 1 0 12 Dec 2022
CAT-probing: A Metric-based Approach to Interpret How Pre-trained Models for Programming Language Attend Code Structure Nuo Chen Qiushi Sun Renyu Zhu Xiang Li Xuesong Lu Ming Gao 31 10 0 07 Oct 2022
Model Reprogramming: Resource-Efficient Cross-Domain Machine Learning Pin-Yu Chen VLM 98 57 0 22 Feb 2022
CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation Yue Wang Weishi Wang Shafiq R. Joty S. Hoi 204 1,451 0 02 Sep 2021
CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation Shuai Lu Daya Guo Shuo Ren Junjie Huang Alexey Svyatkovskiy ... Nan Duan Neel Sundaresan Shao Kun Deng Shengyu Fu Shujie Liu ELM 190 853 0 09 Feb 2021
Pre-trained Models for Natural Language Processing: A Survey Xipeng Qiu Tianxiang Sun Yige Xu Yunfan Shao Ning Dai Xuanjing Huang LM&MA VLM 232 1,444 0 18 Mar 2020