# A Survey of Learning-based Automated Program Repair

arXiv: https://arxiv.org/abs/2301.03270 · HTML: https://mlsanity.com/paper/2301.03270/

## Key facts

- Cited by 55 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jan 2023.

## Built on by (most cited first, 30 of 55)

- [RepairAgent: An Autonomous, LLM-Based Agent for Program Repair](https://mlsanity.com/paper/2403.17134/) (2024)
- [MarsCode Agent: AI-native Automated Bug Fixing](https://mlsanity.com/paper/2409.00899/) (2024)
- [Unifying the Perspectives of NLP and Software Engineering: A Survey on Language Models for Code](https://mlsanity.com/paper/2311.07989/) (2023)
- [Calibration and Correctness of Language Models for Code](https://mlsanity.com/paper/2402.02047/) (2024)
- [An LLM-Assisted Easy-to-Trigger Backdoor Attack on Code Completion Models: Injecting Disguised Vulnerabilities against Strong Detection](https://mlsanity.com/paper/2406.06822/) (2024)
- [An Empirical Study on LLM-based Agents for Automated Bug Fixing](https://mlsanity.com/paper/2411.10213/) (2024)
- [Increasing the LLM Accuracy for Question Answering: Ontologies to the Rescue!](https://mlsanity.com/paper/2405.11706/) (2024)
- [Inside the Scaffold: A Source-Code Taxonomy of Coding Agent Architectures](https://mlsanity.com/paper/2604.03515/) (2026)
- [A Comprehensive Survey on Benchmarks and Solutions in Software Engineering of LLM-Empowered Agentic System](https://mlsanity.com/paper/2510.09721/) (2025)
- [Aligning the Objective of LLM-based Program Repair](https://mlsanity.com/paper/2404.08877/) (2024)
- [TrajAudit: Automated Failure Diagnosis for Agentic Coding Systems](https://mlsanity.com/paper/2605.26563/) (2026)
- [Enhanced Automated Code Vulnerability Repair using Large Language Models](https://mlsanity.com/paper/2401.03741/) (2024)
- [Data Cleaning and Machine Learning: A Systematic Literature Review](https://mlsanity.com/paper/2310.01765/) (2023)
- [VulnLLMEval: A Framework for Evaluating Large Language Models in Software Vulnerability Detection and Patching](https://mlsanity.com/paper/2409.10756/) (2024)
- [RepairBench: Leaderboard of Frontier Models for Program Repair](https://mlsanity.com/paper/2409.18952/) (2024)
- [SGAgent: Suggestion-Guided LLM-Based Multi-Agent Framework for Repository-Level Software Repair](https://mlsanity.com/paper/2602.23647/) (2026)
- [ESALE: Enhancing Code-Summary Alignment Learning for Source Code Summarization](https://mlsanity.com/paper/2407.01646/) (2024)
- [Can GPT-O1 Kill All Bugs? An Evaluation of GPT-Family LLMs on QuixBugs](https://mlsanity.com/paper/2409.10033/) (2024)
- [TransAgent: Enhancing LLM-Based Code Translation via Fine-Grained Execution Alignment](https://mlsanity.com/paper/2409.19894/) (2024)
- [CursorCore: Assist Programming through Aligning Anything](https://mlsanity.com/paper/2410.07002/) (2024)
- [The Art of Repair: Optimizing Iterative Program Repair with Instruction-Tuned Models](https://mlsanity.com/paper/2505.02931/) (2025)
- [Coding Agents Don't Know When to Act](https://mlsanity.com/paper/2605.07769/) (2026)
- [ReCode: Improving LLM-based Code Repair with Fine-Grained Retrieval-Augmented Generation](https://mlsanity.com/paper/2509.02330/) (2025)
- [TraceCoder: A Trace-Driven Multi-Agent Framework for Automated Debugging of LLM-Generated Code](https://mlsanity.com/paper/2602.06875/) (2026)
- [Revisiting and Improving Retrieval-Augmented Deep Assertion Generation](https://mlsanity.com/paper/2309.10264/) (2023)
- [Understanding Defects in Generated Codes by Language Models](https://mlsanity.com/paper/2408.13372/) (2024)
- [ConDefects: A New Dataset to Address the Data Leakage Concern for LLM-based Fault Localization and Program Repair](https://mlsanity.com/paper/2310.16253/) (2023)
- [Improving Deep Assertion Generation via Fine-Tuning Retrieval-Augmented Pre-trained Language Models](https://mlsanity.com/paper/2502.16071/) (2025)
- [Survey of Natural Language Processing for Education: Taxonomy, Systematic Review, and Future Trends](https://mlsanity.com/paper/2401.07518/) (2024)
- [Unlocking LLM Repair Capabilities Through Cross-Language Translation and Multi-Agent Refinement](https://mlsanity.com/paper/2503.22512/) (2025)

## Abstract (arXiv)

Automated program repair (APR) aims to fix software bugs automatically and plays a crucial role in software development and maintenance. With the recent advances in deep learning (DL), an increasing number of APR techniques have been proposed to leverage neural networks to learn bug-fixing patterns from massive open-source code repositories. Such learning-based techniques usually treat APR as a neural machine translation (NMT) task, where buggy code snippets (i.e., source language) are translated into fixed code snippets (i.e., target language) automatically. Benefiting from the powerful capability of DL to learn hidden relationships from previous bug-fixing datasets, learning-based APR techniques have achieved remarkable performance. In this paper, we provide a systematic survey to summarize the current state-of-the-art research in the learning-based APR community. We illustrate the general workflow of learning-based APR techniques and detail the crucial components, including fault localization, patch generation, patch ranking, patch validation, and patch correctness phases. We then discuss the widely-adopted datasets and evaluation metrics and outline existing empirical studies. We discuss several critical aspects of learning-based APR techniques, such as repair domains, industrial deployment, and the open science issue. We highlight several practical guidelines on applying DL techniques for future APR studies, such as exploring explainable patch generation and utilizing code features. Overall, our paper can help researchers gain a comprehensive understanding about the achievements of the existing learning-based APR techniques and promote the practical application of these techniques. Our artifacts are publicly available at urlhttps://github.com/QuanjunZhang/AwesomeLearningAPR.