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CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation

Wang, Y., Wang, W., Joty, S., & Hoi, S. C. H. (2021) · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2021.emnlp-main.685 · Cited by 1,495 · see citing works

Unified Pre-training for Program Understanding and Generation

Ahmad, W., Chakraborty, S., Ray, B., & Chang, K.-W. (2021) · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · doi:10.18653/v1/2021.naacl-main.211 · Cited by 642 · see citing works

Expectation vs. Experience: Evaluating the Usability of Code Generation Tools Powered by Large Language Models

Vaithilingam, P., Zhang, T., & Glassman, E. L. (2022) · CHI Conference on Human Factors in Computing Systems Extended Abstracts · doi:10.1145/3491101.3519665 · Cited by 660 · see citing works

CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation

Lu, S., Guo, D., Ren, S., Huang, J., Svyatkovskiy, A., Blanco, A., Clement, C., Drain, D., Jiang, D., Tang, D., et al. (2021) · arXiv · doi:10.48550/arxiv.2102.04664 · Cited by 536 · see citing works

Large language models for software engineering: survey and open problems

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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

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

Studying the Usage of Text-To-Text Transfer Transformer to Support Code-Related Tasks

Mastropaolo, A., Scalabrino, S., Cooper, N., Palacio, D. N., Poshyvanyk, D., Oliveto, R., & Bavota, G. (2021) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1109/icse43902.2021.00041 · Cited by 271 · see citing works

CodeBLEU: a Method for Automatic Evaluation of Code Synthesis

Ren, S., Guo, D., Lu, S., Zhou, L., Liu, S., Tang, D., Sundaresan, N., Zhou, M., Blanco, A., & Ma, S. (2020) · arXiv · doi:10.48550/arxiv.2009.10297 · Cited by 214 · see citing works

Examining Zero-Shot Vulnerability Repair with Large Language Models

Pearce, H. A., Tan, B., Ahmad, B., Karri, R., & Dolan-Gavitt, B. (2023) · IEEE Symposium on Security and Privacy (S&P) · doi:10.1109/sp46215.2023.10179420 · Cited by 206 · see citing works

Automating code review activities by large-scale pre-training

Li, Z., Lu, S., Guo, D., Duan, N., Jannu, S., Jenks, G., Majumder, D., Green, J., Svyatkovskiy, A., Fu, S., et al. (2022) · ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE) · doi:10.1145/3540250.3549081 · Cited by 226 · see citing works

InferFix: End-to-End Program Repair with LLMs

Jin, M., Shahriar, S., Tufano, M., Shi, X., Lu, S., Sundaresan, N., & Svyatkovskiy, A. (2023) · Proceedings of the 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering · doi:10.1145/3611643.3613892 · Cited by 191 · see citing works

Using pre-trained models to boost code review automation

Tufano, R., Masiero, S., Mastropaolo, A., Pascarella, L., Poshyvanyk, D., & Bavota, G. (2022) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1145/3510003.3510621 · Cited by 182 · 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

Neural Transfer Learning for Repairing Security Vulnerabilities in C Code

Chen, Z., Kommrusch, S., & Monperrus, M. (2022) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2022.3147265 · Cited by 174 · see citing works

Neural program repair with execution-based backpropagation

Ye, H., Martínez, M., & Monperrus, M. (2022) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1145/3510003.3510222 · Cited by 161 · see citing works

An extensive study on pre-trained models for program understanding and generation

Zeng, Z., Tan, H., Zhang, H., Li, J., Zhang, Y., & Zhang, L. (2022) · Proceedings of the 31st ACM SIGSOFT International Symposium on Software Testing and Analysis · doi:10.1145/3533767.3534390 · Cited by 153 · see citing works

On the Robustness of Code Generation Techniques: An Empirical Study on GitHub Copilot

Mastropaolo, A., Pascarella, L., Guglielmi, E., Ciniselli, M., Scalabrino, S., Oliveto, R., & Bavota, G. (2023) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1109/icse48619.2023.00181 · Cited by 149 · see citing works

Towards Automating Code Review Activities

Tufano, R., Pascarella, L., Tufano, M., Poshyvanyk, D., & Bavota, G. (2021) · International Conference on Software Engineering · doi:10.1109/icse43902.2021.00027 · Cited by 230 · see citing works

A Systematic Literature Review on the Use of Deep Learning in Software Engineering Research

Watson, C., Cooper, N., Palacio, D. N., Moran, K., & Poshyvanyk, D. (2022) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3485275 · Cited by 137 · see citing works

A Survey of Learning-based Automated Program Repair

Zhang, Q., Fang, C., Ma, Y., Sun, W., & Chen, Z. (2023) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3631974 · Cited by 153 · see citing works

Natural Language Generation and Understanding of Big Code for AI-Assisted Programming: A Review

Wong, M.-F., Guo, S., Hang, C. N., Ho, S., & Tan, C. W. (2023) · Entropy · doi:10.3390/e25060888 · Cited by 137 · see citing works

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LLaMA-Reviewer: Advancing Code Review Automation with Large Language Models through Parameter-Efficient Fine-Tuning

Lu, J., Yu, L., Xiao-jia, L., Li, Y., & Zuo, C. (2023) · Proceedings/Proceedings - International Symposium on Software Reliability Engineering · doi:10.1109/issre59848.2023.00026 · Cited by 133 · see citing works

ManySStuBs4J Dataset

Karampatsis, R.-M., Sutton, C., Informatics, S. O., Institute for Machine Learning, & Intelligence, D. S. A. A. (2020) · Edinburgh Research Explorer (University of Edinburgh) · doi:10.5281/zenodo.3653444 · Cited by 117 · 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

An Empirical Study on Fine-Tuning Large Language Models of Code for Automated Program Repair

Huang, K., Meng, X., Zhang, J., Liu, Y., Wang, W., Li, S., & Zhang, Y. (2023) · IEEE/ACM International Conference on Automated Software Engineering (ASE) · doi:10.1109/ase56229.2023.00181 · Cited by 113 · see citing works

A Survey on Software Defect Prediction Using Deep Learning

Akimova, E. N., Bersenev, A. Y., Deikov, A. A., Kobylkin, K. S., Konygin, A. V., Mezentsev, I. P., & Misilov, V. E. (2021) · Mathematics · doi:10.3390/math9111180 · Cited by 118 · see citing works

SPT-code

Niu, C., Li, C., Ng, V., Ge, J., Huang, L., & Luo, B. (2022) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1145/3510003.3510096 · Cited by 125 · see citing works

NatGen: generative pre-training by “naturalizing” source code

Chakraborty, S., Ahmed, T., Ding, Y., Devanbu, P. T., & Ray, B. (2022) · Proceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering · doi:10.1145/3540250.3549162 · Cited by 105 · see citing works

On Hardware Security Bug Code Fixes by Prompting Large Language Models

Ahmad, B., Thakur, S., Tan, B., Karri, R., & Pearce, H. A. (2024) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2024.3374558 · Cited by 104 · see citing works

Perfection Not Required? Human-AI Partnerships in Code Translation

Weisz, J. D., Muller, M., Houde, S., Richards, J., Ross, S. I., Martinez, F., Agarwal, M., & Talamadupula, K. (2021) · 26th International Conference on Intelligent User Interfaces · doi:10.1145/3397481.3450656 · Cited by 129 · see citing works

Deep learning-based software engineering: progress, challenges, and opportunities

Chen, X., Hu, X., Huang, Y., Jiang, H., Ji, W., Jiang, Y., Jiang, Y., Liu, B., Liu, H., Li, X., et al. (2024) · Science China Information Sciences · doi:10.1007/s11432-023-4127-5 · Cited by 114 · 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

Keeping Deep Learning Models in Check: A History-Based Approach to Mitigate Overfitting

Li, H., Rajbahadur, G. K., Lin, D., Bezemer, C., & Jiang, Z. M. (2024) · IEEE Access · doi:10.1109/access.2024.3402543 · Cited by 99 · see citing works

Code Generation Using Machine Learning: A Systematic Review

Dehaerne, E., Dey, B., Halder, S., Gendt, S. D., & Meert, W. (2022) · IEEE Access · doi:10.1109/access.2022.3196347 · Cited by 95 · see citing works

An Empirical Study on the Usage of Transformer Models for Code Completion

Ciniselli, M., Cooper, N., Pascarella, L., Mastropaolo, A., Aghajani, E., Poshyvanyk, D., Penta, M. D., & Bavota, G. (2021) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2021.3128234 · Cited by 101 · see citing works

RAP-Gen: Retrieval-Augmented Patch Generation with CodeT5 for Automatic Program Repair

Wang, W.-S., Wang, Y., Joty, S. R., & Hoi, S. (2023) · ESEC/SIGSOFT FSE · doi:10.1145/3611643.3616256 · Cited by 151 · see citing works

SelfAPR: Self-supervised Program Repair with Test Execution Diagnostics

Ye, H., Martínez, M., Luo, X., Zhang, T., & Monperrus, M. (2022) · IEEE/ACM International Conference on Automated Software Engineering (ASE) · doi:10.1145/3551349.3556926 · Cited by 88 · see citing works

An Empirical Study on the Usage of BERT Models for Code Completion

Ciniselli, M., Cooper, N., Pascarella, L., Poshyvanyk, D., Penta, M. D., & Bavota, G. (2021) · International Conference on Mining Software Repositories (MSR) · doi:10.1109/msr52588.2021.00024 · Cited by 93 · see citing works

CoditT5: Pretraining for Source Code and Natural Language Editing

Zhang, J., Panthaplackel, S., Nie, P., Li, J. J., & Gligoric, M. (2022) · IEEE/ACM International Conference on Automated Software Engineering (ASE) · doi:10.1145/3551349.3556955 · Cited by 89 · see citing works

A Deep Dive into Large Language Models for Automated Bug Localization and Repair

Hossain, S. B., Jiang, N., Zhou, Q., Li, X., Chiang, W.-H., Lyu, Y., Nguyen, H. A., & Tripp, O. (2024) · Proceedings of the ACM on software engineering. · doi:10.1145/3660773 · Cited by 84 · see citing works

An Empirical Comparison of Pre-Trained Models of Source Code

Niu, C., Li, C., Ng, V., Chen, D., Ge, J., & Luo, B. (2023) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1109/icse48619.2023.00180 · Cited by 90 · see citing works

Unit Test Case Generation with Transformers and Focal ContextPreprint

Tufano, M., Drain, D., Svyatkovskiy, A., Deng, S. K., & Sundaresan, N. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2009.05617 · Cited by 158 · see citing works

Learning How to Mutate Source Code from Bug-Fixes

Tufano, M., Watson, C., Bavota, G., Penta, M. D., White, M., & Poshyvanyk, D. (2019) · IEEE International Conference on Software Maintenance (ICSM) · doi:10.1109/icsme.2019.00046 · Cited by 74 · see citing works

Using Transfer Learning for Code-Related Tasks

Mastropaolo, A., Cooper, N., Palacio, D. N., Scalabrino, S., Poshyvanyk, D., Oliveto, R., & Bavota, G. (2022) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2022.3183297 · Cited by 74 · see citing works

CCT5: A Code-Change-Oriented Pre-trained Model

Lin, B., Wang, S., Liu, Z., Liu, Y., Xia, X., & Mao, X. (2023) · Proceedings of the 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering · doi:10.1145/3611643.3616339 · Cited by 69 · see citing works

Using deep learning to generate complete log statements

Mastropaolo, A., Pascarella, L., & Bavota, G. (2022) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1145/3510003.3511561 · Cited by 68 · see citing works

ContraBERT: Enhancing Code Pre-trained Models via Contrastive Learning

Liu, S., Wu, B., Xie, X., Meng, G., & Liu, Y. (2023) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1109/icse48619.2023.00207 · Cited by 77 · see citing works

SeqTrans: Automatic Vulnerability Fix Via Sequence to Sequence Learning

Chi, J., Qu, Y., Liu, T., Zheng, Q., & Yin, H. (2022) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2022.3156637 · Cited by 70 · see citing works

Semantic bug seeding: a learning-based approach for creating realistic bugs

Patra, J., & Pradel, M. (2021) · ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE) · doi:10.1145/3468264.3468623 · Cited by 66 · see citing works

DOBF: A Deobfuscation Pre-Training Objective for Programming LanguagesPreprint

Rozière, B., Lachaux, M.-A., Szafraniec, M., & Lample, G. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2102.07492 · Cited by 96 · see citing works

CIRCLE: continual repair across programming languages

Yuan, W., Zhang, Q., He, T., Fang, C., Nguyen, Q. V. H., Hao, X., & Yin, H. (2022) · ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA) · doi:10.1145/3533767.3534219 · Cited by 65 · see citing works

CodeAttack: Code-Based Adversarial Attacks for Pre-trained Programming Language Models

Jha, A., & Reddy, C. K. A. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i12.26739 · Cited by 69 · see citing works

On the Reproducibility and Replicability of Deep Learning in Software Engineering

Liu, C. C., Gao, C., Xia, X., Lo, D., Grundy, J., & Yang, X. (2021) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3477535 · Cited by 64 · see citing works

Pre-Trained Model-Based Automated Software Vulnerability Repair: How Far are We?

Zhang, Q., Fang, C., Yu, B., Sun, W., Zhang, T., & Chen, Z. (2023) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2023.3308897 · Cited by 60 · see citing works

KNOD: Domain Knowledge Distilled Tree Decoder for Automated Program Repair

Jiang, N., Lutellier, T., Lou, Y., Tan, L., Goldwasser, D., & Zhang, X. (2023) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1109/icse48619.2023.00111 · Cited by 61 · see citing works

AI for DevSecOps: A Landscape and Future Opportunities

Fu, M. C., Pasuksmit, J., & Tantithamthavorn, C. K. (2025) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3712190 · Cited by 83 · see citing works

A Survey on Machine Learning Techniques for Source Code AnalysisPreprint

Sharma, T., Kechagia, M., Georgiou, S., Tiwari, R. K., Vats, I., Moazen, H., & Sarro, F. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2110.09610 · Cited by 56 · see citing works

Evaluating SZZ Implementations Through a Developer-Informed Oracle

Rosa, G., Pascarella, L., Scalabrino, S., Tufano, R., Bavota, G., Lanza, M., & Oliveto, R. (2021) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1109/icse43902.2021.00049 · Cited by 50 · see citing works

Automatically Learning Patterns for Self-Admitted Technical Debt Removal

Zampetti, F., Serebrenik, A., & Penta, M. D. (2020) · IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER) · doi:10.1109/saner48275.2020.9054868 · Cited by 51 · see citing works

Code Review Automation: Strengths and Weaknesses of the State of the Art

Tufano, R., Dabić, O., Mastropaolo, A., Ciniselli, M., & Bavota, G. (2024) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2023.3348172 · Cited by 48 · see citing works

On Multi-Modal Learning of Editing Source Code

Chakraborty, S., & Ray, B. (2021) · IEEE/ACM International Conference on Automated Software Engineering (ASE) · doi:10.1109/ase51524.2021.9678559 · Cited by 50 · see citing works

ITER: Iterative Neural Repair for Multi-Location Patches

Ye, H., & Monperrus, M. (2024) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1145/3597503.3623337 · Cited by 48 · see citing works

Recent Advances of Foundation Language Models-based Continual Learning: A Survey

Yang, Y., Zhou, J., Ding, X., Huai, T., Liu, S., Chen, Q., Xie, Y., & He, L. (2024) · ACM Computing Surveys · doi:10.1145/3705725 · Cited by 57 · see citing works

CORE: Resolving Code Quality Issues using LLMs

Wadhwa, N., Pradhan, J., Sonwane, A., Sahu, S. P., Natarajan, N., Kanade, A., Parthasarathy, S., & Rajamani, S. K. (2024) · Proceedings of the ACM on software engineering. · doi:10.1145/3643762 · Cited by 63 · see citing works

Generating bug-fixes using pretrained transformers

Drain, D., Wu, C. H., Svyatkovskiy, A., & Sundaresan, N. (2021) · MAPS@PLDI · doi:10.1145/3460945.3464951 · Cited by 49 · see citing works

On the Effectiveness of Transfer Learning for Code Search

Salza, P., Schwizer, C., Gu, J. N., & Gall, H. C. (2022) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2022.3192755 · Cited by 47 · see citing works

Methods2Test

Tufano, M., Deng, S. K., Sundaresan, N., & Svyatkovskiy, A. (2022) · International Conference on Mining Software Repositories (MSR) · doi:10.1145/3524842.3528009 · Cited by 43 · see citing works

A survey on machine learning techniques applied to source code

Sharma, T., Kechagia, M., Georgiou, S., Tiwari, R., Vats, I., Moazen, H., & Sarro, F. (2023) · Journal of Systems and Software · doi:10.1016/j.jss.2023.111934 · Cited by 130 · see citing works

Prompt-tuned Code Language Model as a Neural Knowledge Base for Type Inference in Statically-Typed Partial Code

Huang, Q. Y., Yuan, Z., Xing, Z., Xu, X., Zhu, L., & Lu, Q. (2022) · IEEE/ACM International Conference on Automated Software Engineering (ASE) · doi:10.1145/3551349.3556912 · Cited by 42 · see citing works

Better Patching Using LLM Prompting, via Self-Consistency

Ahmed, T., & Dévanbu, P. (2023) · IEEE/ACM International Conference on Automated Software Engineering (ASE) · doi:10.1109/ase56229.2023.00065 · Cited by 44 · see citing works

A comprehensive empirical study on bug characteristics of deep learning frameworks

Yang, Y., He, T., Xia, Z., & Feng, Y. (2022) · Information and Software Technology · doi:10.1016/j.infsof.2022.107004 · Cited by 41 · see citing works

Deep Learning Meets Software Engineering: A Survey on Pre-Trained Models of Source Code

Niu, C., Li, C., Luo, B., & Ng, V. (2022) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2022/775 · Cited by 53 · see citing works

Leveraging automated unit tests for unsupervised code translation

Roziere, B., Zhang, J. M., Charton, F., Harman, M., Synnaeve, G., & Lample, G. (2021) · arXiv · doi:10.48550/arxiv.2110.06773 · Cited by 39 · see citing works

Towards AI-Assisted Synthesis of Verified Dafny Methods

Misu, M. R. H., Lopes, C. V., Ma, I., & Noble, J. (2024) · Proceedings of the ACM on Software Engineering · doi:10.1145/3643763 · Cited by 38 · see citing works

State of Practice: LLMs in Software Engineering and Software Architecture

Jahić, J., & Sami, A. (2024) · IEEE International Conference on Software Architecture Companion (ICSA-C) · doi:10.1109/icsa-c63560.2024.00059 · Cited by 38 · see citing works

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(2021) · arXiv (Cornell University) · Cited by 37 · see citing works

Towards JavaScript program repair with generative pre-trained transformer (GPT-2)

Lajkó, M., Csuvik, V., & Vidács, L. (2022) · IEEE/ACM International Workshop on Automated Program Repair (APR) · doi:10.1145/3524459.3527350 · Cited by 38 · see citing works

A structural model for contextual code changes

Brody, S., Alon, U., & Yahav, E. (2020) · Proceedings of the ACM on Programming Languages · doi:10.1145/3428283 · Cited by 36 · see citing works

DeepMerge: Learning to Merge Programs

Dinella, E., Mytkowicz, T., Svyatkovskiy, A., Bird, C., Naik, M., & Lahiri, S. K. (2022) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2022.3183955 · Cited by 36 · see citing works

Template-based Neural Program Repair

Meng, X., Wang, X., Zhang, H., Sun, H., Liu, X., & Hu, C. (2023) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1109/icse48619.2023.00127 · Cited by 36 · see citing works

Learning to Generate Corrective Patches using Neural Machine TranslationPreprint

Hata, H., Shihab, E., & Neubig, G. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1812.07170 · Cited by 36 · see citing works

Unveiling ChatGPT’s usage in open source projects: A mining-based study

Tufano, R., Mastropaolo, A., Pepe, F., Dabic, O., Di Penta, M., & Bavota, G. (2024) · Proceedings of the 21st International Conference on Mining Software Repositories · doi:10.1145/3643991.3644918 · Cited by 38 · see citing works

Cerebro: Static Subsuming Mutant Selection

Garg, A., Ojdanić, M., Degiovanni, R., Chekam, T. T., Papadakis, M., & Traon, Y. L. (2022) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2022.3140510 · Cited by 33 · see citing works

On the Reliability and Explainability of Language Models for Program Generation

Liu, Y., Tantithamthavorn, C. K., Liu, Y., & Li, L. (2024) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3641540 · Cited by 37 · see citing works

Multi-target Backdoor Attacks for Code Pre-trained Models

Li, Y., Liu, S., Chen, K., Xie, X., Zhang, T., & Liu, Y. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.399 · Cited by 37 · see citing works

APPT: Boosting Automated Patch Correctness Prediction via Fine-Tuning Pre-Trained Models

Zhang, Q., Fang, C., Sun, W., Liu, Y., He, T., Hao, X., & Chen, Z. (2024) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2024.3354969 · Cited by 35 · see citing works

A systematic mapping study of source code representation for deep learning in software engineering

Samoaa, P., Bayram, F., Salza, P., & Leitner, P. (2022) · IET Software · doi:10.1049/sfw2.12064 · Cited by 29 · see citing works

Comprehensive Fine-Tuning Large Language Models of Code for Automated Program Repair

Huang, K., Zhang, J., Bao, X., Wang, X., & Liu, Y. (2025) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2025.3532759 · Cited by 30 · see citing works

Machine Learning for Source Code Vulnerability Detection: What Works and What Isn’t There Yet

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Representation Learning for Stack Overflow Posts: How Far Are We?

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Poison Attack and Poison Detection on Deep Source Code Processing Models

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Design pattern recognition: a study of large language models

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Explaining Software Bugs Leveraging Code Structures in Neural Machine Translation

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Learning from what we know: How to perform vulnerability prediction using noisy historical data

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Generating realistic vulnerabilities via neural code editing: an empirical study

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To what extent do deep learning-based code recommenders generate predictions by cloning code from the training set?

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An empirical investigation into learning bug-fixing patches in the wild via neural machine translation

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Learning to Generate Corrective Patches using Neural Machine TranslationPreprint

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Abstraction Mechanism on Neural Machine Translation Models for Automated Program Repair

Wei, M. (2019) · UWSpace (University of Waterloo)

Fix-Filter-Fix: Intuitively Connect Any Models for Effective Bug Fixing

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Learning Code Transformations Via Neural Machine Translation

Tufano, M. (2019) · W&M Publish (College of William & Mary) · doi:10.21220/s2-1z1j-d577