mlsanityData to 7 Oct 2026

DeepCoder: Learning to Write Programs

arXiv 1611.01989 ↗First posted 7 Nov 2016cs.LGcited by 145

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Abstract (arXiv)

We develop a first line of attack for solving programming competition-style problems from input-output examples using deep learning. The approach is to train a neural network to predict properties of the program that generated the outputs from the inputs. We use the neural network's predictions to augment search techniques from the programming languages community, including enumerative search and an SMT-based solver. Empirically, we show that our approach leads to an order of magnitude speedup over the strong non-augmented baselines and a Recurrent Neural Network approach, and that we are able to solve problems of difficulty comparable to the simplest problems on programming competition websites.

Key facts. Cited by 145 papers in the map (data through 7 Oct 2026). First posted on arXiv 7 Nov 2016.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 32; 2022: 28; 2023: 34; 2024: 22; 2025: 17; 2026: 12.

145 papers.

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  1. 2021Evaluating Large Language Models Trained on Codecited by 8,053
  2. 2021Program Synthesis with Large Language Modelscited by 3,509
  3. 2022Competition-Level Code Generation with AlphaCodecited by 1,427
  4. 2022InCoder: A Generative Model for Code Infilling and Synthesiscited by 492
  5. 2022CodeRL: Mastering Code Generation through Pretrained Models and Deep Reinforcement Learningcited by 342
  6. 2021Case-based Reasoning for Natural Language Queries over Knowledge Basescited by 107
  7. 2023Learning Performance-Improving Code Editscited by 103
  8. 2022Language Models Can Teach Themselves to Program Bettercited by 75
  9. 2021From Statistical Relational to Neurosymbolic Artificial Intelligence: a Surveycited by 72
  10. 2024HyperAgent: Generalist Software Engineering Agents to Solve Coding Tasks at Scalecited by 61
  11. 2021Learning to Synthesize Programs as Interpretable and Generalizable Policiescited by 59
  12. 2022Top-Down Synthesis for Library Learningcited by 57
38 more
  1. 2024What Are Tools Anyway? A Survey from the Language Model Perspectivecited by 53
  2. 2021Dorylus: Affordable, Scalable, and Accurate GNN Training with Distributed CPU Servers and Serverless Threadscited by 51
  3. 2025MARFT: Multi-Agent Reinforcement Fine-Tuningcited by 51
  4. 2021Inductive logic programming at 30cited by 46
  5. 2023LILO: Learning Interpretable Libraries by Compressing and Documenting Codecited by 46
  6. 2024AgileCoder: Dynamic Collaborative Agents for Software Development based on Agile Methodologycited by 45
  7. 2021Leveraging Language to Learn Program Abstractions and Search Heuristicscited by 44
  8. 2021SpreadsheetCoder: Formula Prediction from Semi-structured Contextcited by 41
  9. 2023CodeScope: An Execution-based Multilingual Multitask Multidimensional Benchmark for Evaluating LLMs on Code Understanding and Generationcited by 39
  10. 2021Communicating Natural Programs to Humans and Machinescited by 35
  11. 2021Latent Execution for Neural Program Synthesiscited by 35
  12. 2022Towards Data-and Knowledge-Driven Artificial Intelligence: A Survey on Neuro-Symbolic Computingcited by 34
  13. 2025Self-Improving Language Models for Evolutionary Program Synthesis: A Case Study on ARC-AGIcited by 34
  14. 2023Exploring Parameter-Efficient Fine-Tuning Techniques for Code Generation with Large Language Modelscited by 28
  15. 2023Outline, Then Details: Syntactically Guided Coarse-To-Fine Code Generationcited by 27
  16. 2024Is Programming by Example solved by LLMs?cited by 27
  17. 2024A Survey on Multimodal Benchmarks: In the Era of Large AI Modelscited by 27
  18. 2021Programming Puzzlescited by 25
  19. 2023Flows: Building Blocks of Reasoning and Collaborating AIcited by 25
  20. 2021Neural-guided, Bidirectional Program Search for Abstraction and Reasoningcited by 23
  21. 2024CodeIt: Self-Improving Language Models with Prioritized Hindsight Replaycited by 23
  22. 2023InstructABSA: Instruction Learning for Aspect Based Sentiment Analysiscited by 22
  23. 2023Deep Learning for Code Intelligence: Survey, Benchmark and Toolkitcited by 22
  24. 2024LLMs for Relational Reasoning: How Far are We?cited by 21
  25. 2021Generating Adversarial Computer Programs using Optimized Obfuscationscited by 20
  26. 2023Hierarchical Programmatic Reinforcement Learning via Learning to Compose Programscited by 20
  27. 2023Enhancing Network Management Using Code Generated by Large Language Modelscited by 20
  28. 2021PyGlove: Symbolic Programming for Automated Machine Learningcited by 19
  29. 2022CrossBeam: Learning to Search in Bottom-Up Program Synthesiscited by 18
  30. 2024Pix2Code: Learning to Compose Neural Visual Concepts as Programscited by 18
  31. 2022Less is More: Summary of Long Instructions is Better for Program Synthesiscited by 17
  32. 2023ExeDec: Execution Decomposition for Compositional Generalization in Neural Program Synthesiscited by 17
  33. 2023Exploring the Robustness of Large Language Models for Solving Programming Problemscited by 16
  34. 2024SheetAgent: Towards A Generalist Agent for Spreadsheet Reasoning and Manipulation via Large Language Modelscited by 16
  35. 2024HYSYNTH: Context-Free LLM Approximation for Guiding Program Synthesiscited by 14
  36. 2021Neural Program Generation Modulo Static Analysiscited by 13
  37. 2023Neural Task Synthesis for Visual Programmingcited by 13
  38. 2023Explaining Competitive-Level Programming Solutions using LLMscited by 13

50 most cited of 145 All 145 (JSON)