Software — Paper Notes
Code models, summarization, and program analysis
2019
2020
2021
ICLR-2021 GraphCodeBERT:Pre-training Code Representations with Data Flow
EMNLP-2021 CodeT5:Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation
ACL-2021 Learning Sequential and Structural Information for Source Code Summarization
ICSME-2021 Ensemble Models for Neural Source Code Summarization of Subroutines
IJCAI-2021 Graph-Augmented Code Summarization in Computational Notebooks
OpenAI-2021 Evaluating Large Language Models Trained on Code
ICPC-2021 A Multi-Modal Transformer-based Code Summarization Approach for Smart Contracts
Automated Software Engineering-2021 Automating just-in-time comment updating
ACL-2021 Code Summarization with Structure-induced Transformer
2022
EMNLP-2021 HAConvGNN:Hierarchical Attention Based Convolutional Graph Neural Network for Code Documentation Generation in Jupyter Notebooks
ICPC-2022 HELoC:Hierarchical Contrastive Learning of Source Code Representation
EMNLP-2020 CodeBERT:A Pre-Trained Model for Programming and Natural Languages
ICSE-2022 AST-trans:code summarization with efficient tree-structured attention
ICSE-2022 Cross-Domain Deep Code Search with Few-Shot Meta Learning
AAAI-2022 Hierarchical Heterogeneous Graph Attention Network for Syntax-Aware Summarization
arXiv-2022 Learning code summarization from a small and local dataset
Journal of Systems and Software-2022 Automatic source code summarization with graph attention networks
ICML-2016 A Convolutional Attention Network for Extreme Summarization of Source Code
ACL-2022 Impact of Evaluation Methodologies on Code Summarization
NAACL-2022 CODE-MVP:Learning to Represent Source Code from Multiple Views with Contrastive Pre-Training
arXiv-2022 A Survey of Deep Learning Models for Structural Code Understanding