mlsanityData to 7 Oct 2026

SPoC: Search-based Pseudocode to Code

arXiv 1906.04908 ↗First posted 12 Jun 2019cs.LG cs.CL cs.PLcited by 176

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Abstract (arXiv)

We consider the task of mapping pseudocode to long programs that are functionally correct. Given test cases as a mechanism to validate programs, we search over the space of possible translations of the pseudocode to find a program that passes the validation. However, without proper credit assignment to localize the sources of program failures, it is difficult to guide search toward more promising programs. We propose to perform credit assignment based on signals from compilation errors, which constitute 88.7% of program failures. Concretely, we treat the translation of each pseudocode line as a discrete portion of the program, and whenever a synthesized program fails to compile, an error localization method tries to identify the portion of the program responsible for the failure. We then focus search over alternative translations of the pseudocode for those portions. For evaluation, we collected the SPoC dataset (Search-based Pseudocode to Code) containing 18,356 programs with human-authored pseudocode and test cases. Under a budget of 100 program compilations, performing search improves the synthesis success rate over using the top-one translation of the pseudocode from 25.6% to 44.7%.

Key facts. Cited by 176 papers in the map (data through 7 Oct 2026). First posted on arXiv 12 Jun 2019.

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Citing papers per year: 2021: 11; 2022: 20; 2023: 31; 2024: 48; 2025: 46; 2026: 20.

176 papers.

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  1. 2021Evaluating Large Language Models Trained on Codecited by 8,053
  2. 2022PaLM: Scaling Language Modeling with Pathwayscited by 5,304
  3. 2021Program Synthesis with Large Language Modelscited by 3,509
  4. 2024LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Codecited by 1,906
  5. 2022Competition-Level Code Generation with AlphaCodecited by 1,427
  6. 2024Large Language Monkeys: Scaling Inference Compute with Repeated Samplingcited by 946
  7. 2021Measuring Coding Challenge Competence With APPScited by 879
  8. 2022CodeGen: An Open Large Language Model for Code with Multi-Turn Program Synthesiscited by 876
  9. 2021CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generationcited by 732
  10. 2022InCoder: A Generative Model for Code Infilling and Synthesiscited by 492
  11. 2023CodeGeeX: A Pre-Trained Model for Code Generation with Multilingual Benchmarking on HumanEval-Xcited by 294
  12. 2023VerilogEval: Evaluating Large Language Models for Verilog Code Generationcited by 246
38 more
  1. 2024Spider 2.0: Evaluating Language Models on Real-World Enterprise Text-to-SQL Workflowscited by 223
  2. 2023Is Self-Repair a Silver Bullet for Code Generation?cited by 191
  3. 2022Adan: Adaptive Nesterov Momentum Algorithm for Faster Optimizing Deep Modelscited by 172
  4. 2023SantaCoder: don't reach for the stars!cited by 169
  5. 2022Multi-lingual Evaluation of Code Generation Modelscited by 158
  6. 2022MultiPL-E: A Scalable and Extensible Approach to Benchmarking Neural Code Generationcited by 132
  7. 2023FollowBench: A Multi-level Fine-grained Constraints Following Benchmark for Large Language Modelscited by 112
  8. 2023Self-Edit: Fault-Aware Code Editor for Code Generationcited by 110
  9. 2023Large Language Models as Analogical Reasonerscited by 107
  10. 2023A Survey of Text Watermarking in the Era of Large Language Modelscited by 102
  11. 2022Productivity Assessment of Neural Code Completioncited by 97
  12. 2023Execution-based Code Generation using Deep Reinforcement Learningcited by 95
  13. 2023The Robots are Here: Navigating the Generative AI Revolution in Computing Educationcited by 85
  14. 2021Leveraging Automated Unit Tests for Unsupervised Code Translationcited by 82
  15. 2024AnalogCoder: Analog Circuit Design via Training-Free Code Generationcited by 82
  16. 2025Don't Overthink it. Preferring Shorter Thinking Chains for Improved LLM Reasoningcited by 82
  17. 2023Improving Code Generation by Training with Natural Language Feedbackcited by 75
  18. 2024Reuse, Don't Retrain: A Recipe for Continued Pretraining of Language Modelscited by 69
  19. 2024Getting the most out of your tokenizer for pre-training and domain adaptationcited by 65
  20. 2024Are Your LLMs Capable of Stable Reasoning?cited by 65
  21. 2023Large Language Models for Supply Chain Optimizationcited by 61
  22. 2024NExT: Teaching Large Language Models to Reason about Code Executioncited by 60
  23. 2023CodeTransOcean: A Comprehensive Multilingual Benchmark for Code Translationcited by 55
  24. 2024Conformal Prediction for Natural Language Processing: A Surveycited by 52
  25. 2025Diverse Preference Optimizationcited by 48
  26. 2022Compilable Neural Code Generation with Compiler Feedbackcited by 46
  27. 2022Code Translation with Compiler Representationscited by 42
  28. 2023ALGO: Synthesizing Algorithmic Programs with LLM-Generated Oracle Verifierscited by 41
  29. 2024The Larger the Better? Improved LLM Code-Generation via Budget Reallocationcited by 41
  30. 2024CodeJudge: Evaluating Code Generation with Large Language Modelscited by 41
  31. 2026DeepSWE: Measuring Frontier Coding Agents on Original, Long-Horizon Engineering Taskscited by 41
  32. 2025How Do Large Language Monkeys Get Their Power (Laws)?cited by 40
  33. 2023Data Contamination Through the Lens of Timecited by 39
  34. 2021Communicating Natural Programs to Humans and Machinescited by 35
  35. 2021Latent Execution for Neural Program Synthesiscited by 35
  36. 2023SALLM: Security Assessment of Generated Codecited by 35
  37. 2025MultiKernelBench: A Multi-Platform Benchmark for Kernel Generationcited by 35
  38. 2024Mercury: A Code Efficiency Benchmark for Code Large Language Modelscited by 34

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