mlsanityData to 7 Oct 2026

Self-Edit: Fault-Aware Code Editor for Code Generation

arXiv 2305.04087 ↗First posted 6 May 2023cs.SE cs.CLcited by 110

Not on the map. Why?

Cited by 24 in LLM agents and tool use, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Large language models (LLMs) have demonstrated an impressive ability to generate codes on competitive programming tasks. However, with limited sample numbers, LLMs still suffer from poor accuracy. Inspired by the process of human programming, we propose a generate-and-edit approach named Self-Edit that utilizes execution results of the generated code from LLMs to improve the code quality on the competitive programming task. We execute the generated code on the example test case provided in the question and wrap execution results into a supplementary comment. Utilizing this comment as guidance, our fault-aware code editor is employed to correct errors in the generated code. We perform extensive evaluations across two competitive programming datasets with nine different LLMs. Compared to directly generating from LLMs, our approach can improve the average of pass@1 by 89% on APPS-dev, 31% on APPS-test, and 48% on HumanEval over nine popular code generation LLMs with parameter sizes ranging from 110M to 175B. Compared to other post-processing methods, our method demonstrates superior accuracy and efficiency.

Key facts. Cited by 110 papers in the map (data through 7 Oct 2026). Builds on 19 of its 41 references that are in the map. First posted on arXiv 6 May 2023.

Builds on

19 of 41 references.

References · 19

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2021Training Verifiers to Solve Math Word Problemscited by 9,743
  3. 2021Evaluating Large Language Models Trained on Codecited by 8,053
  4. 2022Competition-Level Code Generation with AlphaCodecited by 1,427
  5. 2021Measuring Coding Challenge Competence With APPScited by 879
  6. 2022InCoder: A Generative Model for Code Infilling and Synthesiscited by 492
  7. 2022CodeT: Code Generation with Generated Testscited by 369
  8. 2022A Systematic Evaluation of Large Language Models of Codecited by 360
  9. 2019SPoC: Search-based Pseudocode to Codecited by 176
  10. 2022Natural Language to Code Translation with Executioncited by 122
  11. 2018TRANX: A Transition-based Neural Abstract Syntax Parser for Semantic Parsing and Code Generationcited by 88
  12. 2022CERT: Continual Pre-Training on Sketches for Library-Oriented Code Generationcited by 87
7 more
  1. 2022Coder Reviewer Reranking for Code Generationcited by 83
  2. 2021Generate & Rank: A Multi-task Framework for Math Word Problemscited by 82
  3. 2020Text Generation by Learning from Demonstrationscited by 64
  4. 2022Interactive Code Generation via Test-Driven User-Intent Formalizationcited by 50
  5. 2023SkCoder: A Sketch-based Approach for Automatic Code Generationcited by 40
  6. 2022Fault-Aware Neural Code Rankerscited by 33
  7. 2023AceCoder: Utilizing Existing Code to Enhance Code Generationcited by 25

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 17; 2024: 42; 2025: 30; 2026: 21.

110 papers.

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  1. 2024LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Codecited by 1,906
  2. 2023Large Language Models for Software Engineering: A Systematic Literature Reviewcited by 389
  3. 2024CodeAgent: Enhancing Code Generation with Tool-Integrated Agent Systems for Real-World Repo-level Coding Challengescited by 278
  4. 2023Automatically Correcting Large Language Models: Surveying the landscape of diverse self-correction strategiescited by 236
  5. 2024When Can LLMs Actually Correct Their Own Mistakes? A Critical Survey of Self-Correction of LLMscited by 230
  6. 2023Is Self-Repair a Silver Bullet for Code Generation?cited by 191
  7. 2023LINC: A Neurosymbolic Approach for Logical Reasoning by Combining Language Models with First-Order Logic Proverscited by 161
  8. 2023Structured Chain-of-Thought Prompting for Code Generationcited by 136
  9. 2024Meta-Prompting: Enhancing Language Models with Task-Agnostic Scaffoldingcited by 115
  10. 2024Beyond Functional Correctness: Exploring Hallucinations in LLM-Generated Codecited by 106
  11. 2024Self-Organized Agents: A LLM Multi-Agent Framework toward Ultra Large-Scale Code Generation and Optimizationcited by 84
  12. 2024If LLM Is the Wizard, Then Code Is the Wand: A Survey on How Code Empowers Large Language Models to Serve as Intelligent Agentscited by 77
38 more
  1. 2023Lemur: Harmonizing Natural Language and Code for Language Agentscited by 71
  2. 2024Agents in Software Engineering: Survey, Landscape, and Visioncited by 60
  3. 2023CodeChain: Towards Modular Code Generation Through Chain of Self-revisions with Representative Sub-modulescited by 58
  4. 2024Confidence Matters: Revisiting Intrinsic Self-Correction Capabilities of Large Language Modelscited by 47
  5. 2025Code to Think, Think to Code: A Survey on Code-Enhanced Reasoning and Reasoning-Driven Code Intelligence in LLMscited by 47
  6. 2023Can It Edit? Evaluating the Ability of Large Language Models to Follow Code Editing Instructionscited by 46
  7. 2024From Code to Correctness: Closing the Last Mile of Code Generation with Hierarchical Debuggingcited by 46
  8. 2023Watermarking Conditional Text Generation for AI Detection: Unveiling Challenges and a Semantic-Aware Watermark Remedycited by 45
  9. 2024Iterative Refinement of Project-Level Code Context for Precise Code Generation with Compiler Feedbackcited by 45
  10. 2024Reasoning Runtime Behavior of a Program with LLM: How Far Are We?cited by 44
  11. 2024VLATest: Testing and Evaluating Vision-Language-Action Models for Robotic Manipulationcited by 43
  12. 2023A New Era in Software Security: Towards Self-Healing Software via Large Language Models and Formal Verificationcited by 42
  13. 2023Forward-Backward Reasoning in Large Language Models for Mathematical Verificationcited by 39
  14. 2024CYCLE: Learning to Self-Refine the Code Generationcited by 39
  15. 2024LeDex: Training LLMs to Better Self-Debug and Explain Codecited by 39
  16. 2024AlphaVerus: Bootstrapping Formally Verified Code Generation through Self-Improving Translation and Treefinementcited by 33
  17. 2025CodeCoR: An LLM-Based Self-Reflective Multi-Agent Framework for Code Generationcited by 33
  18. 2024CodeTree: Agent-guided Tree Search for Code Generation with Large Language Modelscited by 32
  19. 2023AgentCoder: Multi-Agent-based Code Generation with Iterative Testing and Optimisationcited by 31
  20. 2023CodeCoT: Tackling Code Syntax Errors in CoT Reasoning for Code Generationcited by 29
  21. 2024EffiLearner: Enhancing Efficiency of Generated Code via Self-Optimizationcited by 28
  22. 2024Search-Based LLMs for Code Optimizationcited by 27
  23. 2024HumanEvalComm: Benchmarking the Communication Competence of Code Generation for LLMs and LLM Agentcited by 24
  24. 2024Tools Fail: Detecting Silent Errors in Faulty Toolscited by 23
  25. 2025QualityFlow: An Agentic Workflow for Program Synthesis Controlled by LLM Quality Checkscited by 22
  26. 2024Investigating the Impact of Data Contamination of Large Language Models in Text-to-SQL Translationcited by 21
  27. 2024A Pair Programming Framework for Code Generation via Multi-Plan Exploration and Feedback-Driven Refinementcited by 21
  28. 2024Reasoning Through Execution: Unifying Process and Outcome Rewards for Code Generationcited by 21
  29. 2024The Counterfeit Conundrum: Can Code Language Models Grasp the Nuances of Their Incorrect Generations?cited by 20
  30. 2025Exploring Autonomous Agents: A Closer Look at Why They Fail When Completing Taskscited by 20
  31. 2025debug-gym: A Text-Based Environment for Interactive Debuggingcited by 19
  32. 2023INTERVENOR: Prompting the Coding Ability of Large Language Models with the Interactive Chain of Repaircited by 18
  33. 2024Selective Prompt Anchoring for Code Generationcited by 16
  34. 2025Learning to Generate Unit Tests for Automated Debuggingcited by 16
  35. 2023Coffee: Boost Your Code LLMs by Fixing Bugs with Feedbackcited by 15
  36. 2024Leveraging Print Debugging to Improve Code Generation in Large Language Modelscited by 15
  37. 2024Beyond Functional Correctness: Investigating Coding Style Inconsistencies in Large Language Modelscited by 15
  38. 2025CoCoEvo: Co-Evolution of Programs and Test Cases to Enhance Code Generationcited by 14

50 most cited of 110 All 110 (JSON)