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Generative Code Modeling with Graphs

arXiv 1805.08490 ↗First posted 22 May 2018cs.LG cs.PL stat.MLcited by 59

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Abstract (arXiv)

Generative models for source code are an interesting structured prediction problem, requiring to reason about both hard syntactic and semantic constraints as well as about natural, likely programs. We present a novel model for this problem that uses a graph to represent the intermediate state of the generated output. The generative procedure interleaves grammar-driven expansion steps with graph augmentation and neural message passing steps. An experimental evaluation shows that our new model can generate semantically meaningful expressions, outperforming a range of strong baselines.

Key facts. Cited by 59 papers in the map (data through 7 Oct 2026). First posted on arXiv 22 May 2018.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 14; 2022: 16; 2023: 11; 2024: 5; 2025: 8; 2026: 5.

59 papers.

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  1. 2022DiGress: Discrete Denoising diffusion for graph generationcited by 503
  2. 2022Score-based Generative Modeling of Graphs via the System of Stochastic Differential Equationscited by 273
  3. 2022DocPrompting: Generating Code by Retrieving the Docscited by 151
  4. 2022A Survey on Deep Graph Generation: Methods and Applicationscited by 69
  5. 2023Graph Generation with Diffusion Mixturecited by 52
  6. 2023Large Language Model-Aware In-Context Learning for Code Generationcited by 42
  7. 2021What do pre-trained code models know about code?cited by 36
  8. 2022StructCoder: Structure-Aware Transformer for Code Generationcited by 33
  9. 2024An LLM-Assisted Easy-to-Trigger Backdoor Attack on Code Completion Models: Injecting Disguised Vulnerabilities against Strong Detectioncited by 32
  10. 2021Code Completion by Modeling Flattened Abstract Syntax Trees as Graphscited by 30
  11. 2023Large Language Models of Code Fail at Completing Code with Potential Bugscited by 28
  12. 2023Outline, Then Details: Syntactically Guided Coarse-To-Fine Code Generationcited by 27
38 more
  1. 2021How could Neural Networks understand Programs?cited by 26
  2. 2023SwinGNN: Rethinking Permutation Invariance in Diffusion Models for Graph Generationcited by 26
  3. 2021A Survey on Machine Learning Techniques for Source Code Analysiscited by 23
  4. 2022A Hazard Analysis Framework for Code Synthesis Large Language Modelscited by 22
  5. 2023Deep Learning for Code Intelligence: Survey, Benchmark and Toolkitcited by 22
  6. 2024IRCoder: Intermediate Representations Make Language Models Robust Multilingual Code Generatorscited by 21
  7. 2023Guiding Language Models of Code with Global Context using Monitorscited by 20
  8. 2021Learning Structural Edits via Incremental Tree Transformationscited by 16
  9. 2022On Distribution Shift in Learning-based Bug Detectorscited by 15
  10. 2021Neural Program Generation Modulo Static Analysiscited by 13
  11. 2024Graph Diffusion Policy Optimizationcited by 12
  12. 2025Flatten Graphs as Sequences: Transformers are Scalable Graph Generatorscited by 12
  13. 2022CS1QA: A Dataset for Assisting Code-based Question Answering in an Introductory Programming Coursecited by 11
  14. 2022Syntax-Aware On-the-Fly Code Completioncited by 10
  15. 2023Discrete Graph Auto-Encodercited by 10
  16. 2021Improving Code Autocompletion with Transfer Learningcited by 6
  17. 2021Embedding API Dependency Graph for Neural Code Generationcited by 5
  18. 2021XFL: Naming Functions in Binaries with Extreme Multi-label Learningcited by 5
  19. 2022GypSum: Learning Hybrid Representations for Code Summarizationcited by 5
  20. 2022TEP-GNN: Accurate Execution Time Prediction of Functional Tests using Graph Neural Networkscited by 5
  21. 2022GraphPNAS: Learning Distribution of Good Neural Architectures via Deep Graph Generative Modelscited by 5
  22. 2023Learning Defect Prediction from Unrealistic Datacited by 5
  23. 2024Neural Graph Generator: Feature-Conditioned Graph Generation using Latent Diffusion Modelscited by 5
  24. 2021Learning to Make Compiler Optimizations More Effectivecited by 4
  25. 2021Toward Less Hidden Cost of Code Completion with Acceptance and Ranking Modelscited by 4
  26. 2022A Survey of Deep Learning Models for Structural Code Understandingcited by 4
  27. 2025ObscuraCoder: Powering Efficient Code LM Pre-Training Via Obfuscation Groundingcited by 4
  28. 2021Text2App: A Framework for Creating Android Apps from Text Descriptionscited by 3
  29. 2021Learning to Extend Program Graphs to Work-in-Progress Codecited by 3
  30. 2022Heterogeneous Graph Neural Networks for Software Effort Estimationcited by 3
  31. 2023Neural Machine Translation for Code Generationcited by 3
  32. 2024Overcoming Order in Autoregressive Graph Generationcited by 3
  33. 2022Exploring and Evaluating Personalized Models for Code Generationcited by 2
  34. 2025SBGD: Improving Graph Diffusion Generative Model via Stochastic Block Diffusioncited by 2
  35. 2021Energy-bounded Learning for Robust Models of Codecited by 1
  36. 2023SANGEA: Scalable and Attributed Network Generationcited by 1
  37. 2025Call Me Maybe: Enhancing JavaScript Call Graph Construction using Graph Neural Networkscited by 1
  38. 2025Seamlessly Integrating Tree-Based Positional Embeddings into Transformer Models for Source Code Representationcited by 1

50 most cited of 59 All 59 (JSON)