GV

GeezerVison

The World Wide Web, restored to 800 pixels of glory.

Home|About|Gaming cabinet|Archives|How it works|Starter sites

On this site

  • Sign in
  • ActionsCI/CD and automation
  • CodespacesCloud development environments
  • All Features
  • Documentation
  • Training
  • Certifications
  • Build with Copilot
  • Customize Copilot
  • Integrations
  • Trending repos
  • GitHub Sponsors
  • Developer articles
  • Enterprises

This view

  • Original page
  • New address

GeezerVison is showing github.com — GitHub - codingClaire/Structural-Code-Understanding: A Survey of Deep Learning Models for Structural Code Understanding

BranchesTags
Open more actions menu

Latest commit

 

History

32 Commits
32 Commits

Folders and files

NameName
Last commit message
Last commit date
graph_based_models
graph_based_models
 
 
sequence_based_models
sequence_based_models
 
 
.DS_Store
.DS_Store
 
 
README.md
README.md
 
 
TODO.md
TODO.md
 
 
dataset_with_link.md
dataset_with_link.md
 
 
graph_based_models.md
graph_based_models.md
 
 
script.py
script.py
 
 
sequence_based_models.md
sequence_based_models.md
 
 
source.md
source.md
 
 

Repository files navigation

Code Understanding Literatures in Deep Learning

Check Our Survey on arxiv!

Sequence-based Models

Total: 44 papers

By years

  • 2021:4 paper(s)
  • 2020:12 paper(s)
  • 2019:7 paper(s)
  • 2018:8 paper(s)
  • 2017:3 paper(s)
  • 2016:6 paper(s)
  • 2014:3 paper(s)
  • 2012:1 paper(s)

By tasks

  • Program Classification :2 paper(s)
  • Code Search:3 paper(s)
  • Code Generation:19 paper(s)
  • Pretrain:4 paper(s)
  • Code representation:1 paper(s)
  • Safety Analysis:4 paper(s)
  • Program Repair :2 paper(s)
  • Clone Detection :2 paper(s)
  • Code Summarization:7 paper(s)

By datasets

  • Java:6 paper(s)
  • DeepFix:1 paper(s)
  • TFix's Code Patches Data:1 paper(s)
  • C:6 paper(s)
  • 9714 Java projects from GitHub:1 paper(s)
  • Python:2 paper(s)
  • JavaScript:1 paper(s)
  • JS150:2 paper(s)
  • python:1 paper(s)
  • Uncategorized:44 paper(s)
  • PY150:2 paper(s)
  • C#:4 paper(s)
  • 10072 Java GitHub repositories:1 paper(s)
  • C#(dataset of CodeNN):0 paper(s)

By models

  • N-gram:3 paper(s)
  • TreeBERT:1 paper(s)
  • Others:1 paper(s)
  • word2vec:2 paper(s)
  • Multinomial Naive Bayes (MNB) :0 paper(s)
  • GRU:3 paper(s)
  • Bi-LSTM:4 paper(s)
  • word embedding:1 paper(s)
  • Transformer:8 paper(s)
  • CRF:1 paper(s)
  • DNN:1 paper(s)
  • pointer network:1 paper(s)
  • DBN:2 paper(s)
  • CAN:1 paper(s)
  • LSTM:15 paper(s)
  • RNN:5 paper(s)

Graph-based Models

Total: 36 papers

By years

  • 2021:9 paper(s)
  • 2020:10 paper(s)
  • 2019:9 paper(s)
  • 2018:3 paper(s)
  • 2017:1 paper(s)
  • 2016:1 paper(s)
  • 2015:1 paper(s)
  • 2014:2 paper(s)

By tasks

  • Defect Prediction:3 paper(s)
  • Code Search:2 paper(s)
  • Program Repair:6 paper(s)
  • Code Generation:5 paper(s)
  • Program Verification:1 paper(s)
  • Program Classification:4 paper(s)
  • Vulnerability Detection:3 paper(s)
  • Clone Detection:8 paper(s)
  • Code Summarization:10 paper(s)

By datasets

  • Java repos collected in this work:1 paper(s)
  • Code-Change-Data:1 paper(s)
  • Hybrid-DeepCom Dataset:1 paper(s)
  • JAVA method naming datasets:1 paper(s)
  • ARM binary dataset:1 paper(s)
  • Genius Dataset:1 paper(s)
  • Google Code Jam (GCJ):0 paper(s)
  • notebookcdg:1 paper(s)
  • program variables dataset produced in this work:1 paper(s)
  • gcc dataset:1 paper(s)
  • Python method documentation dataset:1 paper(s)
  • JS150:2 paper(s)
  • Findutils:1 paper(s)
  • Validation dataset:1 paper(s)
  • Devign Dataset:1 paper(s)
  • C Dataset:1 paper(s)
  • Diffutils:1 paper(s)
  • OpenCL Dataset:1 paper(s)
  • code-comment pairs:1 paper(s)
  • BCB:1 paper(s)
  • collected in this work:4 paper(s)
  • Coreutils:1 paper(s)
  • DeepFix dataset:1 paper(s)
  • Syntax similar dataset:1 paper(s)
  • SPoC:1 paper(s)
  • IJDataset2.0:1 paper(s)
  • CodeForces dataset:1 paper(s)
  • Linux kernel's code collected in this work:1 paper(s)
  • OJClone:5 paper(s)
  • YANCFG Dataset:1 paper(s)
  • Defects4J:1 paper(s)
  • PY150:3 paper(s)
  • iclr18-prog-graphs-dataset:1 paper(s)
  • TL-CodeSum:1 paper(s)
  • CoCoNet:1 paper(s)
  • MSKCFG Dataset:1 paper(s)
  • C Program Dataset:1 paper(s)
  • Java method-comment pairs:1 paper(s)
  • BigCloneBench:2 paper(s)
  • Firmware image dataset:1 paper(s)
  • C# dataset:2 paper(s)
  • CodeSearchNet:2 paper(s)
  • Java Dataset collected in this work:1 paper(s)
  • C dataset:1 paper(s)

By models

  • GINN:1 paper(s)
  • Tree-RNN:1 paper(s)
  • GRU:3 paper(s)
  • Text-associated DeepWalk:1 paper(s)
  • Multi-Relational Graph Neural Network:1 paper(s)
  • TBCNN:1 paper(s)
  • Flow2Vec:1 paper(s)
  • LSTM:5 paper(s)
  • DGCNN:1 paper(s)
  • GNN:11 paper(s)
  • Transformer:3 paper(s)
  • Structure2vec:1 paper(s)
  • MPNN:2 paper(s)
  • GAT:3 paper(s)
  • CNN:7 paper(s)
  • GCN:2 paper(s)
  • RNN:3 paper(s)
  • Tree-LSTM:2 paper(s)
  • tree-based LSTM:1 paper(s)
  • Decision tree:1 paper(s)
  • HAConvGNN:1 paper(s)
  • ConvGNN:2 paper(s)
  • GTN:1 paper(s)
  • GGNN:8 paper(s)
  • CharCNN:1 paper(s)
  • Feed-forward neural network:1 paper(s)
  • bidirectional RNN:1 paper(s)
  • code property graphs:1 paper(s)
  • attention:1 paper(s)
  • Attention mechanism:1 paper(s)

About

A Survey of Deep Learning Models for Structural Code Understanding

Topics

abstract-syntax-treecodecode-generationcode-representationcontrol-flow-graphdeep-neural-networksgnnnlp

Resources

Readme
Activity

Stars

21 stars

Watchers

3 watching

Forks

1 fork
Report repository

Releases

Packages

Contributors

Languages

You are visitor 01402503 — GeezerVison does not store the pages you look up.
Best viewed with Netscape Navigator 4.7 or Internet Explorer 6 at 800×600. No cookies required.