Can OpenAI's codex fix bugs?: an evaluation on QuixBugs

Language Models are Few-Shot Learners

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Scaling Laws for Neural Language Models

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Evaluating Large Language Models Trained on Code

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GenProg: A Generic Method for Automatic Software Repair

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Maintaining mental models: a study of developer work habits

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

Chen, Z.-M., Kommrusch, S., Tufano, M., Pouchet, L., Poshyvanyk, D., & Martin, M. (2018) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2019.2940179 · Cited by 585 · see citing works

Angelix

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CoCoNuT: combining context-aware neural translation models using ensemble for program repair

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Nopol: Automatic Repair of Conditional Statement Bugs in Java Programs

Xuan, J., Martínez, M., DeMarco, F., Clement, M., Marcote, S. R. L., Durieux, T., Berre, D. L., & Monperrus, M. (2016) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2016.2560811 · Cited by 430 · see citing works

CURE: Code-Aware Neural Machine Translation for Automatic Program Repair

Jiang, N., Lutellier, T., & Tan, L. (2021) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1109/icse43902.2021.00107 · Cited by 336 · see citing works

TBar: revisiting template-based automated program repair

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QuixBugs: a multi-lingual program repair benchmark set based on the quixey challenge

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ARJA: Automated Repair of Java Programs via Multi-Objective Genetic Programming

Yuan, Y., & Banzhaf, W. (2018) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2018.2874648 · Cited by 225 · see citing works

Elixir: Effective object-oriented program repair

Saha, R. K., Lyu, Y., Yoshida, H., & Prasad, M. R. (2017) · IEEE/ACM International Conference on Automated Software Engineering (ASE) · doi:10.1109/ase.2017.8115675 · Cited by 193 · see citing works

Leveraging syntax-related code for automated program repair

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HOPPITY: LEARNING GRAPH TRANSFORMATIONS TO DETECT AND FIX BUGS IN PROGRAMS

Dinella, E., Dai, H., Li, Z., Naik, M., Song, L., & Wang, K. (2021) · Figshare · doi:10.6084/m9.figshare.14128508 · Cited by 110 · see citing works

BugsJS: a Benchmark of JavaScript Bugs

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iFixR: bug report driven program repair

Koyuncu, A., Liu, K., Bissyandé, T. F., Kim, D., Monperrus, M., Klein, J., & Traon, Y. L. (2019) · ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE) · doi:10.1145/3338906.3338935 · Cited by 94 · see citing works

LSRepair: Live Search of Fix Ingredients for Automated Program Repair

Liu, K., Koyuncu, A., Kim, K., Kim, D., & Bissyandé, T. F. (2018) · Asia-Pacific Software Engineering Conference (APSEC) · doi:10.1109/apsec.2018.00085 · Cited by 70 · see citing works

A comprehensive study of automatic program repair on the QuixBugs benchmark

Ye, H., Martínez, M., Durieux, T., & Monperrus, M. (2020) · Journal of Systems and Software · doi:10.1016/j.jss.2020.110825 · Cited by 67 · see citing works

A Comprehensive Study of Automatic Program Repair on the QuixBugs Benchmark

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DeepDebug: Fixing Python Bugs Using Stack Traces, Backtranslation, and Code SkeletonsPreprint

Drain, D., Clement, C. B., Serrato, G., & Sundaresan, N. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2105.09352 · Cited by 30 · see citing works

Experience Report: How Effective is Automated Program Repair for Industrial Software?

Noda, K., Nemoto, Y., Hotta, K., Hideo, T., & Kikuchi, S. (2020) · IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER) · doi:10.1109/saner48275.2020.9054829 · Cited by 19 · see citing works

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018) · arXiv · doi:10.48550/arxiv.1810.04805

Software Testing With Large Language Models: Survey, Landscape, and Vision

Wang, J., Huang, Y., Chen, C., Liu, Z., Wang, S., & Wang, Q. (2024) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2024.3368208 · Cited by 573 · see citing works

Code Llama: Open Foundation Models for Code

Rozière, B., Gehring, J., Gloeckle, F., Sootla, S., Gat, I., Tan, X. E., Adi, Y., Liu, J., Sauvestre, R., Remez, T., et al. (2023) · arXiv · doi:10.48550/arxiv.2308.12950 · Cited by 531 · see citing works

Automated Repair of Programs from Large Language Models

Fan, Z., Gao, X., Mirchev, M., Roychoudhury, A., & Tan, S. H. (2023) · 2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE) · doi:10.1109/icse48619.2023.00128 · Cited by 256 · see citing works

Automated Program Repair via Conversation: Fixing 162 out of 337 Bugs for $0.42 Each using ChatGPT

Xia, C. S., & Zhang, L. (2024) · ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA) · doi:10.1145/3650212.3680323 · Cited by 279 · see citing works

AutoCodeRover: Autonomous Program Improvement

Zhang, Y., Ruan, H., Fan, Z., & Roychoudhury, A. (2024) · Proceedings of the 33rd ACM SIGSOFT International Symposium on Software Testing and Analysis · doi:10.1145/3650212.3680384 · Cited by 202 · see citing works

AutoCodeRover: Autonomous Program Improvement

Zhang, Y., Ruan, H., Fan, Z., & Roychoudhury, A. (2024) · arXiv · doi:10.48550/arxiv.2404.05427 · Cited by 202 · see citing works

Impact of Code Language Models on Automated Program Repair

Jiang, N., Liu, K., Lutellier, T., & Tan, L. (2023) · 2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE) · doi:10.1109/icse48619.2023.00125 · Cited by 202 · see citing works

Retrieval-Based Prompt Selection for Code-Related Few-Shot Learning

Nashid, N., Sintaha, M., & Mesbah, A. (2023) · 2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE) · doi:10.1109/icse48619.2023.00205 · Cited by 178 · see citing works

Large Language Models are Few-Shot Summarizers: Multi-Intent Comment Generation via In-Context Learning

Geng, M., Wang, S., Dong, D., Wang, H., Li, G., Jin, Z. G., Mao, X., & Liao, X. (2024) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1145/3597503.3608134 · Cited by 172 · see citing works

Is GitHub’s Copilot as bad as humans at introducing vulnerabilities in code?

Asare, O., Nagappan, M., & Asokan, N. (2023) · Empirical Software Engineering · doi:10.1007/s10664-023-10380-1 · Cited by 128 · see citing works

Towards an understanding of large language models in software engineering tasks

Zheng, Z., Ning, K., Zhong, Q., Chen, J., Chen, W., Guo, L., Wang, W., & Wang, Y. (2024) · Empirical Software Engineering · doi:10.1007/s10664-024-10602-0 · Cited by 149 · see citing works

Copiloting the Copilots: Fusing Large Language Models with Completion Engines for Automated Program Repair

Wei, Y., Xia, C. S., & Zhang, L. (2023) · Proceedings of the 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering · doi:10.1145/3611643.3616271 · Cited by 115 · see citing works

What Makes Good In-Context Demonstrations for Code Intelligence Tasks with LLMs?

Gao, S., Wen, X.-C., Gao, C., Wang, W., Zhang, H., & Lyu, M. R. (2023) · 2023 38th IEEE/ACM International Conference on Automated Software Engineering (ASE) · doi:10.1109/ase56229.2023.00109 · Cited by 106 · see citing works

Refactoring Programs Using Large Language Models with Few-Shot Examples

Shirafuji, A., Oda, Y., Suzuki, J., Morishita, M., & Watanobe, Y. (2023) · Asia-Pacific Software Engineering Conference (APSEC) · doi:10.1109/apsec60848.2023.00025 · Cited by 77 · see citing works

Exploring Parameter-Efficient Fine-Tuning Techniques for Code Generation with Large Language Models

Weyssow, M., Zhou, X., Kim, K., Lo, D., & Sahraoui, H. A. (2025) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3714461 · Cited by 109 · see citing works

ThinkRepair: Self-Directed Automated Program Repair

Yin, X., Ni, C., Wang, S., Li, Z., Zeng, L., & Yang, X. (2024) · Proceedings of the 33rd ACM SIGSOFT International Symposium on Software Testing and Analysis · doi:10.1145/3650212.3680359 · Cited by 68 · see citing works

Domain Knowledge Matters: Improving Prompts with Fix Templates for Repairing Python Type Errors

Peng, Y., Gao, S., Gao, C., Huo, Y., & Lyu, M. R.-T. (2024) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1145/3597503.3608132 · Cited by 53 · see citing works

Does ChatGPT Generate Accessible Code? Investigating Accessibility Challenges in LLM-Generated Source Code

Aljedaani, W., Habib, A., Aljohani, A., Eler, M., & Feng, Y. (2024) · Proceedings of the 21st International Web for All Conference · doi:10.1145/3677846.3677854 · Cited by 42 · see citing works

LLM-Powered Static Binary Taint Analysis

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CREF: An LLM-Based Conversational Software Repair Framework for Programming Tutors

Yang, B., Tian, H., Pian, W., Yu, H., Wang, H., Klein, J., Bissyandé, T. F., & Jin, S. (2024) · Proceedings of the 33rd ACM SIGSOFT International Symposium on Software Testing and Analysis · doi:10.1145/3650212.3680328 · Cited by 41 · see citing works

The Plastic Surgery Hypothesis in the Era of Large Language Models

Xia, C. S., Ding, Y., & Zhang, L. (2023) · 2023 38th IEEE/ACM International Conference on Automated Software Engineering (ASE) · doi:10.1109/ase56229.2023.00047 · Cited by 36 · see citing works

Systematic Literature Review of Prompt Engineering Patterns in Software Engineering

Sasaki, Y., Washizaki, H., Li, J., Sander, D., Yoshioka, N., & Fukazawa, Y. (2024) · IEEE Annual International Computer Software and Applications Conference (COMPSAC) · doi:10.1109/compsac61105.2024.00096 · Cited by 23 · see citing works

LUNA: A Model-Based Universal Analysis Framework for Large Language Models

Song, D., Xie, X., Song, J., Zhu, D., Huang, Y., Juefei-Xu, F., & Ma, L. (2024) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2024.3411928 · Cited by 25 · see citing works

FlakyFix: Using Large Language Models for Predicting Flaky Test Fix Categories and Test Code Repair

Fatima, S., Hemmati, H., & Briand, L. (2024) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2024.3472476 · Cited by 22 · see citing works

Hybrid Automated Program Repair by Combining Large Language Models and Program Analysis

Li, F., Jiang, J., Sun, J., & Zhang, H. (2025) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3715004 · Cited by 30 · see citing works

GPT-3-Powered Type Error Debugging: Investigating the Use of Large Language Models for Code Repair

Ribeiro, F., Macedo, J. N., Tsushima, K., Abreu, R., & Saraiva, J. (2023) · ACM SIGPLAN International Conference on Software Language Engineering (SLE) · doi:10.1145/3623476.3623522 · Cited by 22 · see citing works

Anticipating User Needs: Insights from Design Fiction on Conversational Agents for Computational Thinking

Penney, J., Pimentel, J. F., Steinmacher, I., & Gerosa, M. A. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-54975-5_12 · Cited by 19 · see citing works

Out of Context: How important is Local Context in Neural Program Repair?

Prenner, J. A., & Robbes, R. (2024) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1145/3597503.3639086 · Cited by 22 · see citing works

Agentless: Demystifying LLM-based Software Engineering Agents

Xia, C. S., Deng, Y., Dunn, S., & Zhang, L. (2024) · arXiv · doi:10.48550/arxiv.2407.01489 · Cited by 24 · see citing works

Software Vulnerability Detection with GPT and In-Context Learning

Liu, Z., Liao, Q., Gu, W., & Gao, C. (2023) · IEEE International Conference on Data Science in Cyberspace · doi:10.1109/dsc59305.2023.00041 · Cited by 20 · see citing works

Mutation-based Consistency Testing for Evaluating the Code Understanding Capability of LLMs

Li, Z. H., & Shin, D. (2024) · IEEE/ACM International Conference on AI Engineering – Software Engineering for AI (CAIN) · doi:10.1145/3644815.3644946 · Cited by 18 · see citing works

Let’s Fix this Together: Conversational Debugging with GitHub Copilot

Bajpai, Y., Chopra, B., Biyani, P., Aslan, C., Coleman, D., Gulwani, S., Parnin, C., Radhakrishna, A., & Soares, G. (2024) · IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC) · doi:10.1109/vl/hcc60511.2024.00011 · Cited by 18 · see citing works

REEF: A Framework for Collecting Real-World Vulnerabilities and Fixes

Wang, C., Li, Z., Pena, Y., Gao, S., Chen, S., Wang, S., Gao, C., & Lyu, M. R.-T. (2023) · IEEE/ACM International Conference on Automated Software Engineering (ASE) · doi:10.1109/ase56229.2023.00199 · Cited by 17 · see citing works

Exploring Experiences with Automated Program Repair in Practice

Meem, F. N., Smith, J., & Johnson, B. (2024) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1145/3597503.3639182 · Cited by 17 · see citing works

Aligning the Objective of LLM-Based Program Repair

Xu, J., Fu, Y., Tan, S. H., & He, P. (2025) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1109/icse55347.2025.00169 · Cited by 27 · see citing works

Generative AI for Software Development: A Family of Studies on Code Generation

Dakhel, A. M., Nikanjam, A., Khomh, F., Desmarais, M. C., & Washizaki, H. (2024) · Springer eBooks · doi:10.1007/978-3-031-55642-5_7 · Cited by 16 · see citing works

APIGen: Generative API Method Recommendation

Chen, Y., Gao, C., Zhu, M., Liao, Q., Wang, Y., & Xu, G. (2024) · IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER) · doi:10.1109/saner60148.2024.00025 · Cited by 18 · see citing works

Evaluating ChatGPT for Smart Contracts Vulnerability Correction

Napoli, E. A., & Gatteschi, V. (2023) · IEEE Annual International Computer Software and Applications Conference (COMPSAC) · doi:10.1109/compsac57700.2023.00283 · Cited by 15 · see citing works

An Evaluation of the Effectiveness of OpenAI's ChatGPT for Automated Python Program Bug Fixing using QuixBugs

Wuisang, M. C., Kurniawan, M., Santosa, K. A. W., Gunawan, A. A. S., & Saputra, K. E. (2023) · International Seminar on Application for Technology of Information and Communication (iSemantic) · doi:10.1109/isemantic59612.2023.10295323 · Cited by 14 · see citing works

DrPlanner: Diagnosis and Repair of Motion Planners for Automated Vehicles Using Large Language Models

Lin, Y., Li, C., Ding, M., Tomizuka, M., Zhan, W., & Althoff, M. (2024) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2024.3441493 · Cited by 17 · see citing works

Reality Check: Assessing GPT-4 in Fixing Real-World Software Vulnerabilities

Ságodi, Z., Antal, G., Bogenfürst, B., Isztin, M., Hegedűs, P., & Ferenć, R. (2024) · International Conference on Evaluation and Assessment in Software Engineering (EASE) · doi:10.1145/3661167.3661207 · Cited by 13 · see citing works

MORepair : Teaching LLMs to Repair Code via Multi-Objective Fine-Tuning

Yang, B., Tian, H., Ren, J., Zhang, H., Klein, J., Bissyandé, T. F., Goues, C. L., & Jin, S. (2025) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3735129 · Cited by 25 · see citing works

T5APR: Empowering automated program repair across languages through checkpoint ensemble

Gharibi, R., Sadreddini, M. H., & Fakhrahmad, S. M. (2024) · Journal of Systems and Software · doi:10.1016/j.jss.2024.112083 · Cited by 13 · see citing works

Analyzing Developer Use of ChatGPT Generated Code in Open Source GitHub Projects

Grewal, B., Lu, W., Nadi, S., & Bezemer, C. (2024) · International Conference on Mining Software Repositories (MSR) · doi:10.1145/3643991.3645072 · Cited by 10 · see citing works

Navigating (in)Security of AI-Generated Code

Ambati, S. H., Ridley, N., Branca, E., & Stakhanova, N. (2024) · IEEE International Conference on Cyber Security and Resilience (CSR) · doi:10.1109/csr61664.2024.10679468 · Cited by 10 · see citing works

Statically Contextualizing Large Language Models with Typed Holes

Blinn, A., Li, X., Kim, J. H., & Omar, C. (2024) · Proceedings of the ACM on Programming Languages · doi:10.1145/3689728 · Cited by 10 · see citing works

Context-aware prompting for LLM-based program repair

Li, Y., Cai, M., Chen, J., Xu, Y., Huang, L., & Li, J. K. (2025) · Automated Software Engineering · doi:10.1007/s10515-025-00512-w · Cited by 10 · see citing works

RustAssistant: Using LLMs to Fix Compilation Errors in Rust Code

Deligiannis, P., Lal, A., Mehrotra, N., Poddar, R., & Rastogi, A. (2025) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1109/icse55347.2025.00022 · Cited by 10 · see citing works

Leveraging Large Language Models to Boost Dafny’s Developers Productivity

Silva, Á. F., Mendes, A. S., & Ferreira, J. F. (2024) · IEEE/ACM International Conference on Formal Methods in Software Engineering (FormaliSE) · doi:10.1145/3644033.3644374 · Cited by 10 · see citing works

Hybrid API Migration: A Marriage of Small API Mapping Models and Large Language Models

Zhou, B., Wang, X., Xu, S., Yao, Y., Pan, M., Xu, F., & Ma, X. (2023) · Asia-Pacific Symposium on Internetware · doi:10.1145/3609437.3609466 · Cited by 10 · see citing works

Automated Program Repair for Introductory Programming Assignments

Wan, H., Luo, H., Li, M., & Luo, X. (2024) · IEEE Transactions on Learning Technologies · doi:10.1109/tlt.2024.3403710 · Cited by 10 · see citing works

LLM Assistance for Memory Safety

Mohammed, N., Lal, A., Rastogi, A., Sharma, R., & Roy, S. (2025) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1109/icse55347.2025.00023 · Cited by 9 · see citing works

RAPGen: An Approach for Fixing Code Inefficiencies in Zero-Shot

Garg, S., Moghaddam, R. Z., & Sundaresan, N. (2025) · IEEE/ACM International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP) · doi:10.1109/icse-seip66354.2025.00017 · Cited by 19 · see citing works

An Overview on Large Language Models

Dakhel, A. M., Nikanjam, A., Khomh, F., Desmarais, M. C., & Washizaki, H. (2024) · Springer eBooks · doi:10.1007/978-3-031-55642-5_1 · Cited by 8 · see citing works

The Seeds of the Future Sprout from History: Fuzzing for Unveiling Vulnerabilities in Prospective Deep-Learning Libraries

Li, Z., Wu, J., Ling, X., Luo, T., Rui, Z., & Wu, Y. (2025) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1109/icse55347.2025.00132 · Cited by 9 · see citing works

Program Repair with Minimal Edits Using CodeT5

Shirafuji, A., Rahman, M. M., Amin, M. F. I., & Watanobe, Y. (2023) · International Conference on Awareness Science and Technology (iCAST) · doi:10.1109/icast57874.2023.10359288 · Cited by 10 · see citing works

Automated Program Repair Using Generative Models for Code Infilling

Koutcheme, C., Sarsa, S., Leinonen, J., Hellas, A., & Denny, P. C. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-36272-9_74 · Cited by 8 · see citing works

Evaluating AI-Generated Code for C++, Fortran, Go, Java, Julia, Matlab, Python, R, and Rust

Diehl, P., Nader, N., Brandt, S. R., & Kaiser, H. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-90200-0_20 · Cited by 7 · see citing works

ReAPR: Automatic program repair via retrieval-augmented large language models

Liu, Z., Du, X., & Liu, H. (2025) · Software Quality Journal · doi:10.1007/s11219-025-09728-1 · Cited by 8 · see citing works

RunBugRun: An executable dataset for automated program repair

Prenner, J. A., & Robbes, R. (2026) · Empirical Software Engineering · doi:10.1007/s10664-025-10790-3 · Cited by 11 · see citing works

Exploring the Impact of Intervention Methods on Developers’ Security Behavior in a Manipulated ChatGPT Study

Serafini, R., Yardım, A., & Naiakshina, A. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713989 · Cited by 6 · see citing works

Detecting, Creating, Repairing, and Understanding Indivisible Multi-Hunk Bugs

Xin, Q., Wu, H., Tang, J., Liu, X., Reiss, S. P., & Xuan, J. (2024) · Proceedings of the ACM on software engineering. · doi:10.1145/3660828 · Cited by 6 · see citing works

Enhancing Automated Program Repair with Solution Design

Zhao, J., Yang, D., Zhang, L., Lian, X., Yang, Z., & Liu, F. (2024) · IEEE/ACM International Conference on Automated Software Engineering (ASE) · doi:10.1145/3691620.3695537 · Cited by 6 · see citing works

ROSE: An IDE-Based Interactive Repair Framework for Debugging

Reiss, S. P., Wei, X., Yuan, J. P., & Xin, Q. (2024) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3705306 · Cited by 5 · see citing works

RetypeR: Integrated Retrieval-based Automatic Program Repair for Python Type Errors

Hao, S., Shi, X., & Liu, H. (2024) · IEEE International Conference on Software Maintenance (ICSM) · doi:10.1109/icsme58944.2024.00028 · Cited by 5 · see citing works

Automated Program Repair with the GPT Family, including GPT-2, GPT-3 and CodeX

Lajkó, M., Csuvik, V., Gyimóthy, T., & Vidács, L. (2024) · doi:10.1145/3643788.3648021 · Cited by 5 · see citing works

Context Matters: Investigating Its Impact on ChatGPT's Bug Fixing Performance

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Can GPT-O1 Kill All Bugs? An Evaluation of GPT-Family LLMs on QuixBugs

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NIODebugger: A Novel Approach to Repair Non-Idempotent-Outcome Tests with LLM-Based Agent

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Proceedings of the 2024 IEEE/ACM 12th International Conference on Formal Methods in Software Engineering (FormaliSE)

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Boosting Redundancy-Based Automated Program Repair by Fine-Grained Pattern Mining

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Enhancing Synthetic Test Data Generation with Language Models Using a More Expressive Domain-Specific Language

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SCC-GPT: Source Code Classification Based on Generative Pre-Trained Transformers

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Element-Based Automated DNN Repair with Fine-Tuned Masked Language Model

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Defects4C: Benchmarking Large Language Model Repair Capability with C/C++ Bugs

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DLBench: A Comprehensive Benchmark for SQL Translation with Large Language Models

Lin, L., Chen, H., Zhu, Q., Chen, L., Tang, L., & Wu, R. (2025) · IEEE/ACM International Conference on Automated Software Engineering (ASE) · doi:10.1109/ase63991.2025.00076 · Cited by 2 · see citing works

Unveiling the power of code pre-trained models in automated program repair: A systematic review

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Robustness-Enhanced Assertion Generation Method Based on Code Mutation and Attack Defense

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Benchmarking and Categorizing the Performance of Neural Program Repair Systems for Java

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Exploring ChatGPT's Potential in Java API Method Recommendation: An Empirical Study

Wang, Y., Xue, W., Huang, Q., Jiang, B., & Zhang, H. (2025) · Journal of Software Evolution and Process · doi:10.1002/smr.2765 · Cited by 2 · see citing works

MultiMend: multilingual program repair with context augmentation and multi-hunk patch generation

Gharibi, R., Sadreddini, M. H., & Fakhrahmad, S. M. (2026) · Automated Software Engineering · doi:10.1007/s10515-026-00611-2 · Cited by 3 · see citing works

Extracting Fix Ingredients using Language Models

Prenner, J. A., & Robbes, R. (2025) · IEEE/ACM International Conference on AI Foundation Models and Software Engineering (FORGE) · doi:10.1109/forge66646.2025.00028 · Cited by 1 · see citing works

Patch Generation in APR: A Survey from the Perspectives of Utilizing LLMs and Using APR-Specific Information

Yang, Y., Li, C., Han, Z., Li, R., Xu, K., Li, Q., Zhong, W., Shen, Z., Fei, Z., Ge, J., et al. (2025) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3764584 · Cited by 1 · see citing works

Specification-Guided Repair of Arithmetic Errors in Dafny Programs Using LLMs

Wu, V., Mendes, A. S., & Abreu, A. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-10444-1_16 · Cited by 1 · see citing works

InstructRepair: Instruct Large Language Models With Rich Bug Information for Automated Program Repair

Fu, A., Xu, P., Li, J., Kuang, B., & Gao, Y. (2025) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2025.3618407 · Cited by 1 · see citing works

HFuzzer: Testing Large Language Models for Package Hallucinations via Phrase-based Fuzzing

Zhao, Y., Wu, M., Hu, X., & Xia, X. (2025) · IEEE/ACM International Conference on Automated Software Engineering (ASE) · doi:10.1109/ase63991.2025.00225 · Cited by 1 · see citing works

Securing AI Code Generation - A Prompt Rectification Approach for Mitigating Cyber Risks

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Towards defect-type-aware adaptive program repair: A stage-wise approach with large language models

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HAFix: history-augmented large language models for bug fixing

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Model See, Model Do? Exposure-Aware Evaluation of Bug-vs-Fix Preference in Code LLMs

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PReMM: LLM-Based Program Repair for Multi-method Bugs via Divide and Conquer

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Demonstration Attack against In-Context Learning for Code Intelligence

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EvoAPR: Enhancing Large Language Models for Automatic Program Repair with Genetic Algorithm and Dynamic LoRA

Zhang, H., Yan, Q., Min, W., Zhang, C., Kuang, L., & Xia, Y. (2025) · IEEE International Conference on Web Services (ICWS) · doi:10.1109/icws67624.2025.00123

An Empirical Study Measuring In-The-Wild Cryptographic Microarchitectural Side-Channel Patches

Deng, S., Liu, Z., Wang, S., & Zhang, Y. (2025) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/3719027.3744881

Fine-Grained Bug Localization in Software Projects Using Code Graph

Sunitha, E. V. (2026) · Lecture notes in networks and systems · doi:10.1007/978-981-96-9275-0_38

How Small is Enough? Empirical Evidence of Quantized Small Language Models for Automated Program Repair

Kusama, K., Shu, H., Kondo, M., & Kamei, Y. (2025) · Proceedings of the ... ACM-IEEE International Symposium on Empirical Software Engineering and Measurement/International Symposium on Empirical Software Engineering and Measurement · doi:10.1109/esem64174.2025.00047

FACKT: A Fault-Aware Model for Code Knowledge Tracing

Zhou, M., Zhou, Q., Xia, W., Jiang, Y., Lin, C., & Zhao, S. (2026) · Lecture notes in computer science · doi:10.1007/978-981-95-5719-6_5

Yazılım Mühendisliği Eğitiminde Üretken Yapay Zekânın Bibliyometrik Analiz Yöntemi ile İncelenmesi

Baz, F. Ç., & Keleş, Ş. (2026) · Osmaniye Korkut Ata Üniversitesi Fen Bilimleri Enstitüsü Dergisi · doi:10.47495/okufbed.1766554

Impact of Different Buggy Code Information on Large Language Model-Based Automated Program Repair

Liu, L., Li, M., & Ren, J. (2026) · International Symposium on Computer Technology and Information Science (ISCTIS) · doi:10.1109/isctis70043.2026.11572607

Fine-Grained Repair Knowledge Organization for Knowledge-Guided Automated Program Repair

Jiang, J., Cheng, H., Liu, Y., Lin, N., Yu, S., Zhu, X., & Wang, J. (2026) · Lecture notes in computer science · doi:10.1007/978-981-92-3450-9_50

Automatic Program Repair with OpenAI's Codex: Evaluating QuixBugs

Prenner, J. A., & Robbes, R. (2021) · arXiv · doi:10.48550/arxiv.2111.03922 · Cited by 25 · see citing works

Framing program repair as code completion

Ribeiro, F., Abreu, R., & Saraiva, J. (2022) · IEEE/ACM International Workshop on Automated Program Repair (APR) · doi:10.1145/3524459.3527347 · Cited by 12 · see citing works

Investigating large language models capabilities for automatic code repair in Python

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An Analysis of the Automatic Bug Fixing Performance of ChatGPT

Sobania, D., Briesch, M., Hanna, C., & Petke, J. (2023) · 2023 IEEE/ACM International Workshop on Automated Program Repair (APR) · doi:10.1109/apr59189.2023.00012 · Cited by 338 · see citing works

Code Generation and Automated Bug Fixing

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