mlsanityData to 7 Oct 2026

Code Completion by Modeling Flattened Abstract Syntax Trees as Graphs

arXiv 2103.09499 ↗First posted 17 Mar 2021cs.SE cs.AI cs.CLcited by 30

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Abstract (arXiv)

Code completion has become an essential component of integrated development environments. Contemporary code completion methods rely on the abstract syntax tree (AST) to generate syntactically correct code. However, they cannot fully capture the sequential and repetitive patterns of writing code and the structural information of the AST. To alleviate these problems, we propose a new code completion approach named CCAG, which models the flattened sequence of a partial AST as an AST graph. CCAG uses our proposed AST Graph Attention Block to capture different dependencies in the AST graph for representation learning in code completion. The sub-tasks of code completion are optimized via multi-task learning in CCAG, and the task balance is automatically achieved using uncertainty without the need to tune task weights. The experimental results show that CCAG has superior performance than state-of-the-art approaches and it is able to provide intelligent code completion.

Key facts. Cited by 30 papers in the map (data through 7 Oct 2026). Builds on 28 of its 39 references that are in the map. First posted on arXiv 17 Mar 2021.

Builds on

28 of 39 references.

References · 28

  1. 2017Attention Is All You Needcited by 59,932
  2. 2015Deep Residual Learning for Image Recognitioncited by 51,809
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  4. 2017Graph Attention Networkscited by 8,854
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  7. 2017Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semanticscited by 1,345
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  9. 2017GradNorm: Gradient Normalization for Adaptive Loss Balancing in Deep Multitask Networkscited by 816
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16 more
  1. 2017Learning to Represent Programs with Graphscited by 219
  2. 2018code2seq: Generating Sequences from Structured Representations of Codecited by 177
  3. 2020Improved Code Summarization via a Graph Neural Networkcited by 79
  4. 2020Code Prediction by Feeding Trees to Transformerscited by 70
  5. 2018Generative Code Modeling with Graphscited by 59
  6. 2020Deep Learning for Source Code Modeling and Generation: Models, Applications and Challengescited by 55
  7. 2019Pythia: AI-assisted Code Completion Systemcited by 54
  8. 2017Code Completion with Neural Attention and Pointer Networkscited by 49
  9. 2020Fast and Memory-Efficient Neural Code Completioncited by 38
  10. 2018A General Path-Based Representation for Predicting Program Propertiescited by 28
  11. 2020Multi-Task Learning for Dense Prediction Tasks: A Surveycited by 28
  12. 2019A Self-Attentional Neural Architecture for Code Completion with Multi-Task Learningcited by 18
  13. 2016Learning Python Code Suggestion with a Sparse Pointer Networkcited by 12
  14. 2020Sequence Model Design for Code Completion in the Modern IDEcited by 8
  15. 2019TreeCaps: Tree-Structured Capsule Networks for Program Source Code Processingcited by 4
  16. 2020Improving the Robustness to Data Inconsistency between Training and Testing for Code Completion by Hierarchical Language Modelcited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 6; 2023: 4; 2024: 11; 2025: 5; 2026: 4.

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  1. 2024A Survey on Large Language Models for Code Generationcited by 646
  2. 2023Execution-based Code Generation using Deep Reinforcement Learningcited by 95
  3. 2024LLM Hallucinations in Practical Code Generation: Phenomena, Mechanism, and Mitigationcited by 92
  4. 2024Agents in Software Engineering: Survey, Landscape, and Visioncited by 60
  5. 2024GraphCoder: Enhancing Repository-Level Code Completion via Code Context Graph-based Retrieval and Language Modelcited by 40
  6. 2023Natural Language Generation and Understanding of Big Code for AI-Assisted Programming: A Reviewcited by 32
  7. 2023Deep Learning for Code Intelligence: Survey, Benchmark and Toolkitcited by 22
  8. 2025What to Retrieve for Effective Retrieval-Augmented Code Generation? An Empirical Study and Beyondcited by 22
  9. 2022CoCoSoDa: Effective Contrastive Learning for Code Searchcited by 21
  10. 2024Beyond Functional Correctness: Investigating Coding Style Inconsistencies in Large Language Modelscited by 15
  11. 2025CodeRAG: Finding Relevant and Necessary Knowledge for Retrieval-Augmented Repository-Level Code Completioncited by 12
  12. 2022Syntax-Aware On-the-Fly Code Completioncited by 10
18 more
  1. 2024Does Your Neural Code Completion Model Use My Code? A Membership Inference Approachcited by 9
  2. 2024Investigating the Performance of Language Models for Completing Code in Functional Programming Languages: a Haskell Case Studycited by 7
  3. 2026AlignCoder: Aligning Retrieval with Target Intent for Repository-Level Code Completioncited by 7
  4. 2022CAT-probing: A Metric-based Approach to Interpret How Pre-trained Models for Programming Language Attend Code Structurecited by 6
  5. 2022A Neural Network Architecture for Program Understanding Inspired by Human Behaviorscited by 5
  6. 2022A Survey of Deep Learning Models for Structural Code Understandingcited by 4
  7. 2022A Library for Representing Python Programs as Graphs for Machine Learningcited by 4
  8. 2023Code Search Debiasing:Improve Search Results beyond Overall Ranking Performancecited by 4
  9. 2024Structured Code Representations Enable Data-Efficient Adaptation of Code Language Modelscited by 3
  10. 2024AnyBipe: An End-to-End Framework for Training and Deploying Bipedal Robots Guided by Large Language Modelscited by 3
  11. 2024Renaissance of Literate Programming in the Era of LLMs: Enhancing LLM-Based Code Generation in Large-Scale Projectscited by 3
  12. 2026DRAINCODE: Stealthy Energy Consumption Attacks on Retrieval-Augmented Code Generation via Context Poisoningcited by 3
  13. 2025CCCI: Code Completion with Contextual Information for Complex Data Transfer Tasks Using Large Language Modelscited by 2
  14. 2026PhoenixRepair: Rethinking Repair Strategy Exploration in Software Agentscited by 2
  15. 2024FuncEvalGMN: Evaluating Functional Correctness of SQL via Graph Matching Networkcited by 1
  16. 2025SwiftEval: Developing a Language-Specific Benchmark for LLM-generated Code Evaluationcited by 0
  17. 2025A Matter of Representation: Towards Graph-Based Abstract Code Generationcited by 0
  18. 2026Adaptive Critical Token-Aware Retrieval for Repository-Level Code Generationcited by 0