title | year | venue | task | model | dataset | code | ||
---|---|---|---|---|---|---|---|---|
0 | HAConvGNN: Hierarchical Attention Based Convolutional Graph Neural Network for Code Documentation Generation in Jupyter Notebooks | 2021 | EMNLP | Code Summarization | HAConvGNN | notebookcdg | 📑 | ![]() |
1 | Code Completion by Modeling Flattened Abstract Syntax Trees as Graphs | 2021 | AAAI | Code Generation | GAT | JS150, PY150 | 📑 | |
2 | CAST: Enhancing Code Summarization with Hierarchical Splitting and Reconstruction of Abstract Syntax Trees | 2021 | EMNLP | Code Summarization | RNN,attention | TL-CodeSum | 📑 | ![]() |
3 | Code Clone Detection with Hierarchical Attentive Graph Embedding | 2021 | International Journal of Software Engineering and Knowledge Engineering | Clone Detection | GCN | IJDataset2.0 | 📑 | |
4 | Improving Code Summarization with Block-wise Abstract Syntax Tree Splitting | 2021 | ICPC | Code Summarization | Tree-LSTM | CodeSearchNet, Hybrid-DeepCom Dataset | 📑 | ![]() |
5 | BugGraph: Differentiating Source-Binary Code Similarity with Graph Triplet-Loss Network | 2021 | ASIA CCS | Vulnerability Detection | GTN | Validation dataset, Syntax similar dataset, ARM binary dataset, Firmware image dataset | 📑 | |
6 | How could Neural Networks understand Programs? | 2021 | ICML | Clone Detection | Transformer | OJClone | 📑 | ![]() |
7 | CoCoSum: Contextual Code Summarization with Multi-Relational Graph Neural Network | 2021 | arxiv | Code Summarization | Transformer,Multi-Relational Graph Neural Network | CodeSearchNet, CoCoNet | 📑 | |
8 | Retrieval-Augmented Generation for Code Summarization via Hybrid GNN | 2021 | ICLR | Code Summarization | GNN | C Program Dataset | 📑 | ![]() |
title | year | venue | task | model | dataset | code | ||
---|---|---|---|---|---|---|---|---|
0 | CODIT: Code Editing with Tree-Based Neural Models | 2020 | IEEE Transactions on Software Engineering | Program Repair | LSTM | Defects4J,Code-Change-Data | 📑 | ![]() |
1 | GGF: A graph-based method for programming language syntax error correction | 2020 | ICPC | Program Repair | GGNN | DeepFix dataset,CodeForces dataset | 📑 | |
2 | Improved code summarization via a graph neural network | 2020 | ICPC | Code Summarization | ConvGNN | Java method-comment pairs | 📑 | |
3 | Graph-based, Self-Supervised Program Repair from Diagnostic Feedback | 2020 | ICML | Program Repair | GAT, LSTM | SPoC | 📑 | ![]() |
4 | DeepBinDiff: Learning Program-Wide Code Representations for Binary Diffing | 2020 | NDSS | Clone Detection | Text-associated DeepWalk | Coreutils, Diffutils, Findutils | 📑 | ![]() |
5 | Order matters: Semantic-aware neural networks for binary code similarity detection | 2020 | AAAI | Clone Detection | MPNN,CNN | gcc dataset | 📑 | |
6 | Learning semantic program embeddings with graph interval neural network | 2020 | Proceedings of the ACM on Programming Languages | Program Repair | GINN | PY150 | 📑 | |
7 | Semantic Code Clone Detection Via Event Embedding Tree and GAT Network | 2020 | QRS | Clone Detection | Transformer, GAT, CNN | OJClone | 📑 | ![]() |
8 | Flow2Vec:value-flow-based precise code embedding | 2020 | Proceedings of the ACM on Program ming Languages | Code Summarization, Program Classification | Flow2Vec | C Dataset | 📑 | |
9 | Compiler-based graph representations for deep learning models of code | 2020 | Proceedings of the 29th International Conference on Compiler Construction | Program Classification | GGNN | OpenCL Dataset | 📑 | ![]() |
title | year | venue | task | model | dataset | code | ||
---|---|---|---|---|---|---|---|---|
0 | A Novel Neural Source Code Representation Based on Abstract Syntax Tree | 2019 | ICSE | Program Classification, Clone Detection | bidirectional RNN | OJClone,BCB | 📑 | ![]() |
1 | Capturing Source Code Semantics via Tree-based Convolution over API-enhanced AST | 2019 | ACM International Conference on Computing Frontiers | Clone Detection,Code Search, Code Summarization | tree-based LSTM | OJClone,BigCloneBench | 📑 | ![]() |
2 | Structured neural summarization | 2019 | ICLR | Code Summarization | GGNN | C# dataset,JAVA method naming datasets, Python method documentation dataset | 📑 | ![]() |
3 | Generative code modeling with graphs | 2019 | ICLR | Program Repair,Code Generation | GRU,GGNN | C# dataset | 📑 | ![]() |
4 | Classifying Malware Represented as Control Flow Graphs using Deep Graph Convolutional Neural Network | 2019 | Annual IEEE/IFIP International Conference on Dependable Systems and Networks, DSN | Defect Prediction | DGCNN | MSKCFG Dataset, YANCFG Dataset | 📑 | |
5 | Multi-modal attention network learning for semantic source code retrieval | 2019 | ASE | Code Search | GGNN, Tree-LSTM | C dataset | 📑 | |
6 | Devign: Effective Vulnerability Identification by Learning Comprehensive Program Semantics via Graph Neural Networks | 2019 | NIPS | Vulnerability Detection | GGNN, GRU, CNN | Devign Dataset | 📑 | ![]() |
7 | Improving bug detection via context-based code representation learning and attention-based neural networks | 2019 | Proceedings of the ACM on Programming Languages | Defect Prediction | GRU, CNN, Attention mechanism | Java Dataset collected in this work | 📑 | |
8 | Open vocabulary learning on source code with a graph-structured caches | 2019 | ICML | Code Generation | MPNN,CharCNN | Java repos collected in this work | 📑 | ![]() |
title | year | venue | task | model | dataset | code | ||
---|---|---|---|---|---|---|---|---|
0 | Learning to represent programs with graphs | 2018 | ICLR | Defect Prediction,Code Generation | GGNN | iclr18-prog-graphs-dataset | 📑 | ![]() |
1 | Improving automatic source code summarization via deep reinforcement learning | 2018 | ASE | Code Summarization | RNN,Tree-RNN | code-comment pairs | 📑 | |
2 | DeepSim: Deep Learning Code Functional Similarity | 2018 | ESEC/FSE | Clone Detection | Feed-forward neural network | Google Code Jam (GCJ), BigCloneBench | 📑 | ![]() |
title | year | venue | task | model | dataset | code | ||
---|---|---|---|---|---|---|---|---|
0 | Neural network-based graph embedding for cross-platform binary code similarity detection | 2017 | Proceedings of the ACM Conference on Computer and Communications Security | Program Repair | Structure2vec | collected in this work, Genius Dataset | 📑 | ![]() |
title | year | venue | task | model | dataset | code | ||
---|---|---|---|---|---|---|---|---|
0 | Probabilistic model for code with decision trees | 2016 | SIGPLAN | Code Generation | Decision tree | PY150, JS150 | 📑 |
title | year | venue | task | model | dataset | code | ||
---|---|---|---|---|---|---|---|---|
0 | Gated graph sequence neural networks | 2015 | ICLR | Program Verification | GGNN | program variables dataset produced in this work | 📑 | ![]() |
title | year | venue | task | model | dataset | code | ||
---|---|---|---|---|---|---|---|---|
0 | TBCNN: A tree-based convolutional neural network for programming language processing | 2014 | arixiv | Program Classification | TBCNN | OJClone | 📑 | ![]() |
1 | Modeling and discovering vulnerabilities with code property graphs | 2014 | Proceedings IEEE Symposium on Security and Privacy | Vulnerability Detection | code property graphs | Linux kernel's code collected in this work | 📑 |