# A Survey on Automated Program Repair Techniques

arXiv: https://arxiv.org/abs/2303.18184 · HTML: https://mlsanity.com/paper/2303.18184/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Mar 2023.

## Built on by (most cited first, 13 of 13)

- [Unifying the Perspectives of NLP and Software Engineering: A Survey on Language Models for Code](https://mlsanity.com/paper/2311.07989/) (2023)
- [CigaR: Cost-efficient Program Repair with LLMs](https://mlsanity.com/paper/2402.06598/) (2024)
- [A Controlled Experiment on the Energy Efficiency of the Source Code Generated by Code Llama](https://mlsanity.com/paper/2405.03616/) (2024)
- [Enhancing Automated Program Repair with Solution Design](https://mlsanity.com/paper/2408.12056/) (2024)
- [LProtector: An LLM-driven Vulnerability Detection System](https://mlsanity.com/paper/2411.06493/) (2024)
- [Planning-Driven Programming: A Large Language Model Programming Workflow](https://mlsanity.com/paper/2411.14503/) (2024)
- [Agentic Program Repair from Test Failures at Scale: A Neuro-symbolic approach with static analysis and test execution feedback](https://mlsanity.com/paper/2507.18755/) (2025)
- [Frustrated with Code Quality Issues? LLMs can Help!](https://mlsanity.com/paper/2309.12938/) (2023)
- [The Impact of Fine-tuning Large Language Models on Automated Program Repair](https://mlsanity.com/paper/2507.19909/) (2025)
- [ACE-RLHF: Automated Code Evaluation and Socratic Feedback Generation Tool using Large Language Models and Reinforcement Learning with Human Feedback](https://mlsanity.com/paper/2504.04657/) (2025)
- [A Metamorphic Testing Approach to Diagnosing Memorization in LLM-Based Program Repair](https://mlsanity.com/paper/2604.21579/) (2026)
- [A Tertiary Review of Large Language Model-Based Code Generating Tasks: Trends, Challenges, and Future Directions](https://mlsanity.com/paper/2605.25536/) (2026)
- [Security in LLM-Assisted Coding: A Systematic Literature Review](https://mlsanity.com/paper/2610.15482/) (2026)

## Abstract (arXiv)

With the rapid development and large-scale popularity of program software, modern society increasingly relies on software systems. However, the problems exposed by software have also come to the fore. Software defect has become an important factor troubling developers. In this context, Automated Program Repair (APR) techniques have emerged, aiming to automatically fix software defect problems and reduce manual debugging work. In particular, benefiting from the advances in deep learning, numerous learning-based APR techniques have emerged in recent years, which also bring new opportunities for APR research. To give researchers a quick overview of APR techniques' complete development and future opportunities, we revisit the evolution of APR techniques and discuss in depth the latest advances in APR research. In this paper, the development of APR techniques is introduced in terms of four different patch generation schemes: search-based, constraint-based, template-based, and learning-based. Moreover, we propose a uniform set of criteria to review and compare each APR tool, summarize the advantages and disadvantages of APR techniques, and discuss the current state of APR development. Furthermore, we introduce the research on the related technical areas of APR that have also provided a strong motivation to advance APR development. Finally, we analyze current challenges and future directions, especially highlighting the critical opportunities that large language models bring to APR research.