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codingClaire/Structural-Code-Understanding

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Code Understanding Literatures in Deep Learning

Check Our Survey on arxiv!

Sequence-based Models

Total: 44 papers

  • 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)
  • 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)
  • 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)
  • 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

  • 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)
  • 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)
  • 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)
  • 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)

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A Survey of Deep Learning Models for Structural Code Understanding

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