mlsanityData to 7 Oct 2026

SequenceR: Sequence-to-Sequence Learning for End-to-End Program Repair

arXiv 1901.01808 ↗First posted 24 Dec 2018cs.SE cs.LG stat.MLcited by 144

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Abstract (arXiv)

This paper presents a novel end-to-end approach to program repair based on sequence-to-sequence learning. We devise, implement, and evaluate a system, called SequenceR, for fixing bugs based on sequence-to-sequence learning on source code. This approach uses the copy mechanism to overcome the unlimited vocabulary problem that occurs with big code. Our system is data-driven; we train it on 35,578 samples, carefully curated from commits to open-source repositories. We evaluate it on 4,711 independent real bug fixes, as well on the Defects4J benchmark used in program repair research. SequenceR is able to perfectly predict the fixed line for 950/4711 testing samples, and find correct patches for 14 bugs in Defects4J. It captures a wide range of repair operators without any domain-specific top-down design.

Key facts. Cited by 144 papers in the map (data through 7 Oct 2026). First posted on arXiv 24 Dec 2018.

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Citing papers per year: 2021: 26; 2022: 18; 2023: 39; 2024: 21; 2025: 19; 2026: 21.

144 papers.

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  1. 2021Program Synthesis with Large Language Modelscited by 3,509
  2. 2022InCoder: A Generative Model for Code Infilling and Synthesiscited by 492
  3. 2024Agentless: Demystifying LLM-based Software Engineering Agentscited by 400
  4. 2021Unified Pre-training for Program Understanding and Generationcited by 361
  5. 2024When Can LLMs Actually Correct Their Own Mistakes? A Critical Survey of Self-Correction of LLMscited by 230
  6. 2024RepairAgent: An Autonomous, LLM-Based Agent for Program Repaircited by 225
  7. 2021CodeNet: A Large-Scale AI for Code Dataset for Learning a Diversity of Coding Taskscited by 199
  8. 2023Is Self-Repair a Silver Bullet for Code Generation?cited by 191
  9. 2024Large Language Model-Based Agents for Software Engineering: A Surveycited by 173
  10. 2023Keep the Conversation Going: Fixing 162 out of 337 bugs for 0.42 each using ChatGPTcited by 125
  11. 2023Baldur: Whole-Proof Generation and Repair with Large Language Modelscited by 118
  12. 2023RLTF: Reinforcement Learning from Unit Test Feedbackcited by 110
38 more
  1. 2021Examining Zero-Shot Vulnerability Repair with Large Language Modelscited by 105
  2. 2023Execution-based Code Generation using Deep Reinforcement Learningcited by 95
  3. 2021CVEfixes: Automated Collection of Vulnerabilities and Their Fixes from Open-Source Softwarecited by 89
  4. 2021CURE: Code-Aware Neural Machine Translation for Automatic Program Repaircited by 85
  5. 2021Leveraging Automated Unit Tests for Unsupervised Code Translationcited by 82
  6. 2021Break-It-Fix-It: Unsupervised Learning for Program Repaircited by 71
  7. 2021DOBF: A Deobfuscation Pre-Training Objective for Programming Languagescited by 68
  8. 2024Aligning LLM Agents by Learning Latent Preference from User Editscited by 66
  9. 2024NExT: Teaching Large Language Models to Reason about Code Executioncited by 60
  10. 2021A Syntax-Guided Edit Decoder for Neural Program Repaircited by 57
  11. 2023Copiloting the Copilots: Fusing Large Language Models with Completion Engines for Automated Program Repaircited by 55
  12. 2023How Effective Are Neural Networks for Fixing Security Vulnerabilitiescited by 50
  13. 2023RepairLLaMA: Efficient Representations and Fine-Tuned Adapters for Program Repaircited by 50
  14. 2023LLaMA-Reviewer: Advancing Code Review Automation with Large Language Models through Parameter-Efficient Fine-Tuningcited by 47
  15. 2022Compilable Neural Code Generation with Compiler Feedbackcited by 46
  16. 2023Large Language Models and Simple, Stupid Bugscited by 45
  17. 2023RAP-Gen: Retrieval-Augmented Patch Generation with CodeT5 for Automatic Program Repaircited by 43
  18. 2023A New Era in Software Security: Towards Self-Healing Software via Large Language Models and Formal Verificationcited by 42
  19. 2024Can LLMs Replace Manual Annotation of Software Engineering Artifacts?cited by 40
  20. 2021Neural Transfer Learning for Repairing Security Vulnerabilities in C Codecited by 36
  21. 2024Automatic Programming: Large Language Models and Beyondcited by 35
  22. 2023Natural Language Generation and Understanding of Big Code for AI-Assisted Programming: A Reviewcited by 32
  23. 2023Better patching using LLM prompting, via Self-Consistencycited by 29
  24. 2023Large Language Models of Code Fail at Completing Code with Potential Bugscited by 28
  25. 2021On Multi-Modal Learning of Editing Source Codecited by 25
  26. 2021A Survey on Machine Learning Techniques for Source Code Analysiscited by 23
  27. 2023Deep Learning for Code Intelligence: Survey, Benchmark and Toolkitcited by 22
  28. 2021Generating Bug-Fixes Using Pretrained Transformerscited by 18
  29. 2021DeepDebug: Fixing Python Bugs Using Stack Traces, Backtranslation, and Code Skeletonscited by 17
  30. 2024PromSec: Prompt Optimization for Secure Generation of Functional Source Code with Large Language Models (LLMs)cited by 17
  31. 2021Learning Structural Edits via Incremental Tree Transformationscited by 16
  32. 2023ITER: Iterative Neural Repair for Multi-Location Patchescited by 16
  33. 2023GrACE: Generation using Associated Code Editscited by 16
  34. 2023Towards Generating Functionally Correct Code Edits from Natural Language Issue Descriptionscited by 14
  35. 2023Benchmarking Causal Study to Interpret Large Language Models for Source Codecited by 14
  36. 2025Beyond Final Code: A Process-Oriented Error Analysis of Software Development Agents in Real-World GitHub Scenarioscited by 14
  37. 2023KNOD: Domain Knowledge Distilled Tree Decoder for Automated Program Repaircited by 13
  38. 2023Toward a Theory of Causation for Interpreting Neural Code Modelscited by 12

50 most cited of 144 All 144 (JSON)