Automated Bug Detection and Program Repair Using Deep Learning: A Comprehensive Review

Rawaa Hamza Ali; Adala Mahdi Chyad

doi:10.2478/cait-2026-0006

References

Fang, C., Q. Zhang, W. Sun, Y. Ma, Z. Chen. Survey of Learning-Based Automated Program Repair. – ACM Trans. Software Eng. Methodol., Vol. 33, 2023, No 2, pp. 1-69.
Search in Google Scholar Back to article
Ye, H., M. Monperrus. Iter: Iterative Neural Repair for Multi-Location Patches. – In: Proc. of 46th IEEE/ACM Int. Conf. on Software Engineering (ICSE’24), 2024, pp. 1-13.
Search in Google Scholar Back to article
Kim, Y., Y. Park, S. Han, J. Yi. Enhancing the Efficiency of Automated Program Repair via Greybox Analysis. – In: Proc. of 39th IEEE/ACM Int. Conf. on Automated Software Engineering (ASE’24), 2024, pp. 1719-1731.
Search in Google Scholar Back to article
Chen, X., D. Zhang, Y. Zhao, Z. Cui, C. Ni. Software Defect Number Prediction: Unsupervised vs Supervised Methods. – Inf. Software Technol., Vol. 106, 2019, pp. 161-181.
Search in Google Scholar Back to article
Durieux, T., F. Madeiral, M. Martinez, R. Abreu. Empirical Review of Java Program Repair Tools: A Large-Scale Experiment on 2141 Bugs and 23,551 Repair Attempts. – In: Proc. of ESEC/FSE’2019, 2019, pp. 302-313.
Search in Google Scholar Back to article
Rana, M. R. R., A. Nawaz, T. Ali, A. S. Alattas, D. S. AbdElminaam. Sentiment Analysis of Product Reviews Using Transformer-Enhanced 1D-CNN and BiLSTM. – Cybernetics and Information Technologies, Vol. 24, 2024, No 3, pp. 450-465.
Search in Google Scholar Back to article
Feng, Z., D. Guo, D. Tang, N. Duan, X. Feng, M. Zhou, L. Shou. CodeBERT: A Pre-Trained Model for Programming and Natural Languages. – In: Findings of the Association for Computational Linguistics (EMNLP’2020), 2020, pp. 1536-1547.
Search in Google Scholar Back to article
Jabrw, S. K., Q. I. Sarhan. A Systematic Survey on Large Language Models for Code Generation. – ARO – The Scientific Journal of Koya University, Vol. 13, 2025, No 2, pp. 83-99.
Search in Google Scholar Back to article
Dikici, S., T. T. Bilgin. Advancements in Automated Program Repair: A Comprehensive Review. – Knowledge and Information Systems, Vol. 67, 2025, No 6, pp. 4737-4783.
Search in Google Scholar Back to article
Yin, X., C. Ni, S. Wang, Z. Li, L. Zeng, X. Yang. ThinkRepair: Self-Directed Automated Program Repair. – In: Proc. of 33rd ACM SIGSOFT Int. Symp. on Software Testing and Analysis (ISSTA’24), 2024, pp. 1274-1286.
Search in Google Scholar Back to article
Kong, J., X. Xie, S. Liu. Demystifying Memorization in LLM-Based Program Repair via a General Hypothesis Testing Framework. – Proc. ACM Software Eng., Vol. 2, 2025, No FSE, pp. 2712-2734.
Search in Google Scholar Back to article
Guan, H., G. Bai, Y. Liu. CrossProbe: LLM-Empowered Cross-Project Bug Detection for Deep Learning Frameworks. – Proc. ACM Software Eng., Vol. 2, 2025, No ISSTA, pp. 2430-2452.
Search in Google Scholar Back to article
Farid, A. B., E. M. Fathy, A. S. Eldin, L. A. Abd-Elmegid. Software Defect Prediction Using a Hybrid Model (CBIL) of CNN and Bi-LSTM. – PeerJ Comput. Sci., Vol. 7, 2021, p. e739.
Search in Google Scholar Back to article
Xu, W., C. Huang, S. Gao, S. Shang. LLM-Based Agents for Tool Learning: A Survey. – Data Sci. Eng., 2025, pp. 1-31.
Search in Google Scholar Back to article
Saavedra, N., A. Silva, M. Monperrus. GitBug-Actions: Building Reproducible Bug-Fix Benchmarks with GitHub Actions. – In: Proc. ICSE Companion, 2024, pp. 1-5.
Search in Google Scholar Back to article
Yang, D., etal. Where Were the Repair Ingredients for Defects4J Bugs? Exploring the Impact of Repair Ingredient Retrieval on the Performance of 24 Program Repair Systems. – Empir. Software Eng., Vol. 26, 2021, No 6, p. 122.
Search in Google Scholar Back to article
Rafi, M. N., A. R. Chen, T.-H. P. Chen, S. Wang. Revisiting Defects4J for Fault Localization in Diverse Development Scenarios. – In: Proc. of 22nd IEEE/ACM Int. Conf. on Mining Software Repositories (MSR’25), 2025, pp. 63-75.
Search in Google Scholar Back to article
Lopez-Duran, N., D. Romero-Organvidez, F. L. Cruz, D. Benavides. Software Bug Report Dataset from Eclipse Projects. – Data in Brief, Vol. 62, 2025, 112016.
Search in Google Scholar Back to article
Ferenc, R., Z. Tóth, G. Ladányi, I. Siket, T. Gyimóthy. A Public Unified Bug Dataset for Java and Its Assessment Regarding Metrics and Bug Prediction. – Software Qual. J., Vol. 28, 2020, pp. 1447-1506.
Search in Google Scholar Back to article
Saha, R. K., Y. Lyu, W. Lam, H. Yoshida, M. R. Prasad. Bugs.jar: A Large-Scale, Diverse Dataset of Real-World Java Bugs. – In: Proc. of 15th Int. Conf. on Mining Software Repositories (MSR’18), 2018, pp. 10-13.
Search in Google Scholar Back to article
Madeiral, F., S. Urli, M. Maia, M. Monperrus. Bears: An Extensible Java Bug Benchmark for Automatic Program Repair Studies. – In: Proc. of 26th IEEE Int. Conf. on Software Analysis, Evolution and Reengineering (SANER’19), 2019, pp. 468-478.
Search in Google Scholar Back to article
Karampatsis, R.-M., C. Sutton. How Often Do Single-Statement Bugs Occur? The Many SStuBs4J Dataset. – In: Proc. of 17th Int. Conf. on Mining Software Repositories (MSR’20), 2020, pp. 573-577.
Search in Google Scholar Back to article
Abdollahpour, M. M., M. Ashtiani, F. Bakhshi. Automatic Software Code Repair Using Deep Learning Techniques. – Software Qual. J., Vol. 32, 2024, No 2, pp. 361-390.
Search in Google Scholar Back to article
Li, X., D. Li, M. Zhao, W. E. Wong, H. Li. Learning-Based Patch Overfitting Detection: A Survey. – J. Internet Technol., Vol. 26, 2025, No 1, pp. 53-64
Search in Google Scholar Back to article
Le, X. B. D., F. Thung, D. Lo, C. LeGoues. Overfitting in Semantics-Based Automated Program Repair. – Empirical Software Engineering, Vol. 23, 2018, No 5, pp. 2941-2973.
Search in Google Scholar Back to article
Liu, K., etal. TrickyBugs: A Dataset of Corner-Case Bugs in Plausible Programs. – In: Proc. of 21st Int. Conf. on Mining Software Repositories (MSR’24), 2024, pp. 113-117.
Search in Google Scholar Back to article
Tan, S. H., J. Yi, S. Mechtaev, A. Roychoudhury. Codeflaws: A Programming Competition Benchmark for Evaluating Automated Program Repair Tools. – In: Proc. of ICSE-Companion, 2017, pp. 180-182.
Search in Google Scholar Back to article
Lin, D., J. Koppel, A. Chen, A. Solar-Lezama. QuixBugs: A Multi-Lingual Program Repair Benchmark Set Based on the Quixey Challenge. – In: Proc. of SPLASH Companion, 2017, pp. 55-56.
Search in Google Scholar Back to article
Ye, H., M. Martinez, T. Durieux, M. Monperrus. A Comprehensive Study of Automatic Program Repair on the QuixBugs Benchmark. – J. Syst. Softw., Vol. 171, 2021, 110825.
Search in Google Scholar Back to article
Ahmed, A., H. Qaiser. Bug Classification Using CNN-LSTM in Open-Source Software Systems. – Computer Science Review, Vol. 37, Article 2020, 100234.
Search in Google Scholar Back to article
Khurma, R. A., H. Alsawalqah, I. Aljarah, M. A. Elaziz, R. Damaševičius. An Enhanced Evolutionary Software Defect Prediction Method Using Island Moth Flame Optimization. – Mathematics, Vol. 9, 2021, No 15, 1722.
Search in Google Scholar Back to article
Rahim, A., Z. Hayat, M. Abbas, A. Rahim, M. A. Rahim. Software Defect Prediction with Naïve Bayes Classifier. – In: Proc. of Int. Bhurban Conf. on Applied Sciences and Technologies (IBCAST’21), 2021, pp. 293-297.
Search in Google Scholar Back to article
Uddin, M. N., B. Li, Z. Ali, P. Kefalas, I. Khan, I. Zada. Software Defect Prediction Employing BiLSTM and BERT-Based Semantic Feature. – Software Comput., Vol. 26, 2022, No 16, pp. 7877-7891.
Search in Google Scholar Back to article
Zaidi, S. F. A., H. Woo, C.-G. Lee. A Graph Convolution Network-Based Bug Triage System to Learn Heterogeneous Graph Representation of Bug Reports. – IEEE Access, Vol. 10, 2022, pp. 20677-20689.
Search in Google Scholar Back to article
Lin, B., R. Wang, Z. Shen, J. Jiang, L. Li, X. Gu. One Size Does Not Fit All: Multi-Granularity Patch Generation for Better Automated Program Repair. – In: Proc. of 46th International Conference on Software Engineering (ICSE’24), IEEE/ACM, 2024, pp. 1546-1557. DOI: 10.1145/3597503.3623406.
Search in Google Scholar Back to article
Xia, C. S., L. Zhang. Automated Program Repair via Conversation: Fixing 162 out of 337 Bugs for $0.42 Each Using ChatGPT. – In: Proc. of ISSTA’24, 2024, pp. 819-831.
Search in Google Scholar Back to article
Chen, H. SynergyBug: A Deep Learning Approach to Autonomous Debugging and Code Remediation. – Scientific Reports, Vol. 15, 2025,  24888. DOI: 10.1038/s41598-025-08226-5.
Search in Google Scholar Back to article
Bouzenia, I., P. Devanbu, M. Pradel. RepairAgent: An Autonomous LLM-Based Agent for Program Repair. – Proceedings of the ACM on Software Engineering, Vol. 2, 2025, No ICSE, pp. 1-15.
Search in Google Scholar Back to article
Al-Bataineh, O. I., L. Moonen, L. Vidziunas. Extending the Range of Bugs that Automated Program Repair Can Handle. – J. Syst. Software, Vol. 209, 2024, 111918.
Search in Google Scholar Back to article
Liu, K., etal. A Critical Review on the Evaluation of Automated Program Repair Systems. – J. Syst. Software, Vol. 171, 2021, 110817.
Search in Google Scholar Back to article
Ouyang, Y., J. Yang, L. Zhang. An Empirical Study on the Suitability of Test-Based Patch Acceptance Criteria. – ACM Trans. Software Eng. Methodol., Vol. 34, 2025, No 4, pp. 1-28.
Search in Google Scholar Back to article
Oyo-Ita, E., E. A. Edim, A. O. Otiko, D. E. Izuki. Improving Model Performance for Software Defect Detection and Prediction Using an Ensemble Method and Cross-Validation Techniques. – Int. J. Sci. Res. Archive, Vol. 12, 2024, No 2, 10.30574.
Search in Google Scholar Back to article
Kang, S., J. Wang, T. Zhang, G. Meng, Z. Wang. AutoSD: Explainable Automated Debugging with Large Language Models. – Empirical Software Engineering, Vol. 30, 2025, No 2, pp. 1-32.
Search in Google Scholar Back to article
Bello, R.-W., S. J. Tobi. Software Bugs: Detection, Analysis, and Fixing. – Analysis and Fixing, 2023.
Search in Google Scholar Back to article
Niu, F., C. Li, K. Liu, X. Xia, D. Lo. When Deep Learning Meets IR-Based Bug Localization: A Survey. – ACM Comput. Surv., Vol. 57, 2025, No 11, pp. 1-41.
Search in Google Scholar Back to article
Akimova, E. N., et al. A Survey on Software Defect Prediction Using Deep Learning. – Mathematics, Vol. 9, 2021, No 11, 1180.
Search in Google Scholar Back to article
Zhang, H., Z. Li, J. Li, Z. Jin, G. Li. WELL: Applying Bug Detectors to Bug Localization via Weakly Supervised Learning. – J. Software: Evol. Process, Vol. 36, 2024, No 9, e2669.
Search in Google Scholar Back to article
Viswanadhapalli, V. Automated Bug Detection and Resolution Using Deep Learning: A New Paradigm in Software Engineering. – Int. J. Eng. Comput. Sci., Vol. 13, 2024, No 4.
Search in Google Scholar Back to article
Prasad, R. D., M. Srivenkatesh. Evaluating RNNs and Transformers for Code-Related Tasks, Including Bug Detection, Code Completion, and Summarization. – J. Theor. Appl. Inf. Technol., Vol. 102, 2024, No 21.
Search in Google Scholar Back to article
Kukkar, A., R. Mohana, A. Nayyar, J. Kim, B.-G. Kang, N. Chilamkurti. A Novel Deep-Learning-Based Bug Severity Classification Technique Using CNN and Random Forest with Boosting. – Sensors, Vol. 19, 2019, No 13, 2964.
Search in Google Scholar Back to article
Hoffman, I., N. Brooks. Automated Bug Detection and Correction in Software Development Using Machine Learning. – Int. J. Adv. Comput. Theory Eng., Vol. 12, 2023, No 1, pp. 15-21.
Search in Google Scholar Back to article
Kesavan, E. Software Bug Prediction Using Machine Learning Algorithms: An Empirical Study on Code Quality and Reliability. – Int. J. Innovations Sci. Eng. Manag., 2025, pp. 377-381.
Search in Google Scholar Back to article
Albattah, W., M. Alzahrani. Software Defect Prediction Based on Machine Learning and Deep Learning Techniques: An Empirical Approach. – AI, Vol. 5, 2024, No 4, pp. 1743-1758.
Search in Google Scholar Back to article
Parvathy, R., M. Thushara. AST-Based and Token-Based Neural Networks for Source Code Classification: A Comparative Performance Analysis. – In: Proc. of 15th Int. Conf. on Computing Communication and Networking Technologies (ICCCNT’24), 2024, pp. 1-7.
Search in Google Scholar Back to article
Zhang, X., M. Guo, Z. Chen, Y. Zhang, T. Ju, B. Xiao. Exploring Extended Abstract Syntax Tree Encoding for Enhancing Code Vulnerability Detection. – SSRN 4951333.
Search in Google Scholar Back to article
Li, Z., D. Zou, S. Xu, H. Jin, Y. Zhu, Z. Chen. SySeVR: A Framework for Using Deep Learning to Detect Software Vulnerabilities. – IEEE Trans. Dependable Secure Comput., Vol. 19, 2021, No 4, pp. 2244-2258.
Search in Google Scholar Back to article
Zaidi, S. M. H., M. Khan, M. Latif, A. Akhtar, S. Khan. Use of Deep Learning in Early Software Bug Detection. – Mehran Univ. Res. J. Eng. Technol., Vol. 44, 2025, No 3, pp. 141-151.
Search in Google Scholar Back to article
Pradel, M., K. Sen. DeepBugs: A Learning Approach to Name-Based Bug Detection. – Proc. ACM Program. Lang., Vol. 2, 2018, No OOPSLA, pp. 1-25.
Search in Google Scholar Back to article
Yang, Y., X. Xia, D. Lo, J. Grundy. A Survey on Deep Learning for Software Engineering. – ACM Comput. Surv., Vol. 54, 2022, No 10s, pp. 1-73.
Search in Google Scholar Back to article
Casey, B., J. C. Santos, G. Perry. A Survey of Source Code Representations for ML-Based Cybersecurity Tasks. – ACM Comput. Surv., Vol. 57, 2025, No 8, pp. 1-41.
Search in Google Scholar Back to article
Zhang, J., etal. Detecting Condition-Related Bugs with a Control-Flow Graph Neural Network. – In: Proc. of ISSTA’23, 2023, pp. 1370-1382.
Search in Google Scholar Back to article
Zhou, Y., S. Liu, J. Siow, X. Du, Y. Liu. Devign: Effective Vulnerability Identification by Learning Comprehensive Program Semantics via Graph Neural Networks. – NeurIPS, Vol. 32, 2019.
Search in Google Scholar Back to article
Fu, M., C. Tantithamthavorn. LineVul: A Transformer-Based Line-Level Vulnerability Prediction. – In: Proc. of 19th Int. Conf. on Mining Software Repositories (MSR’22), 2022, pp. 608-620.
Search in Google Scholar Back to article
Cheng, X., H. Wang, J. Hua, G. Xu, Y. Sui. DeepWukong: Statically Detecting Software Vulnerabilities Using a Deep Graph Neural Network. – ACM Trans. Softw. Eng. Methodol. (TOSEM), Vol. 30, 2021, No 3, pp. 1-33.
Search in Google Scholar Back to article
DeKraker, W., H. Vranken, A. Hommersom. MultiGLICE: Combining Graph Neural Networks and Program Slicing for Multiclass Software Vulnerability Detection. – Computers, Vol. 14, 2025, No 3, p. 98.
Search in Google Scholar Back to article
Pan, C., M. Lu, B. Xu. An Empirical Study on Software Defect Prediction Using the CodeBERT Model. – Appl. Sci., Vol. 11, 2021, No 11, 4793.
Search in Google Scholar Back to article
Li, Y., etal. A Knowledge-Enhanced Large Language Model for Bug Localization. – Proc. ACM Softw. Eng., Vol. 2, 2025, No FSE, pp. 1914-1936.
Search in Google Scholar Back to article
Wang, Y., W. Wang, S. Joty, S. C. Hoi. CodeT5: Identifier-Aware Unified Pre-Trained Encoder-Decoder Models for Code Understanding and Generation. – arXiv:2109.00859, 2021.
Search in Google Scholar Back to article
Huang, K., J. Zhang, X. Bao, X. Wang, Y. Li u. Comprehensive Fine-Tuning of Large Language Models of Code for Automated Program Repair. – IEEE Trans. Software Eng., 2025.
Search in Google Scholar Back to article
Vokhranov, I., B. Bulakh. Transformer-Based Models Application for Bug Detection in Source Code. – Technol. Audit Prod. Reserves, Vol. 5, 2024, No 2(79), pp. 6-15.
Search in Google Scholar Back to article
Moenks, N., P. Penava, R. Buettner. A Systematic Literature Review of Large Language Model Applications in Industry. – IEEE Access, 2025.
Search in Google Scholar Back to article
Chen, Z., S. Kommrusch, M. Tufano, L.-N. Pouchet, D., Poshyvanyk, M., Monperrus. Sequencer: Sequence-to-Sequence Learning for End-to-End Program Repair. – IEEE Trans. Software Eng., Vol. 47, 2019, No 9, pp. 1943-1959.
Search in Google Scholar Back to article
Vassilev, M., V. Vassilev, A. Penev. IDD – A Platform Enabling Differential Debugging. – Cybernetics and Information Technologies, Vol. 20, 2020, No 1, pp. 29-43.
Search in Google Scholar Back to article
Lutellier, T., H. V. Pham, L. Pang, Y. Li, M. Wei, L. Tan. COCONUT: Combining Context-Aware Neural Translation Models Using an Ensemble for Program Repair. – In: Proc. of ISSTA’20, 2020, pp. 101-114.
Search in Google Scholar Back to article
Li, Y., S. Wang, T. N. Nguyen. DEAR: A Novel Deep Learning-Based Approach for Automated Program Repair. – In: Proc. of 44th Int. Conf. on Software Engineering (ICSE’22), 2022, pp. 511-523.
Search in Google Scholar Back to article
Liu, K., A. Koyuncu, D. Kim, T. F. Bissyandé. TBar: Revisiting Template-Based Automated Program Repair. – In: Proc. of ISSTA’19, 2019, pp. 31-42.
Search in Google Scholar Back to article
Huang, S., X. Zhou, S. Chin. Application of seq2seq Models on Code Correction. – Front. Artif. Intell., Vol. 4, 2021, 590215.
Search in Google Scholar Back to article
DMonperrus, M. Sequencer: Sequence-to-Sequence Learning for End-to-End Program Repair. – IEEE Transactions on Software Engineering, Vol. 47, 2019, No 9, pp. 1943-1959.
Search in Google Scholar Back to article
Hossain, S. B., et al. A Deep Dive into Large Language Models for Automated Bug Localization and Repair. – Proc. ACM Software Eng., Vol. 1, 2024, No FSE, pp. 1471-1493.
Search in Google Scholar Back to article
Xia, C. S., L. Zhang. Less Training, More Repairing Please: Revisiting Automated Program Repair via Zero-Shot Learning. – In: Proc. of ESEC/FSE’22, 2022, pp. 959-971.
Search in Google Scholar Back to article
Li, F., J. Jiang, J. Sun, H. Zhang. Hybrid Automated Program Repair by Combining Large Language Models and Program Analysis. – ACM Trans. Software Eng. Methodol., Vol. 34, 2025, No 7, pp. 1-28.
Search in Google Scholar Back to article
Jiang, N., T. Lutellier, L. Tan. CURE: Code-Aware Neural Machine Translation for Automatic Program Repair. – In: Proc. of ICSE’21, 2021, pp. 1161-1173.
Search in Google Scholar Back to article
Ye, H., M. Martinez, M. Monperrus. Automated Patch Assessment for Program Repair at Scale. – Empir. Software Eng., Vol. 26, 2021, No 2, p. 20.
Search in Google Scholar Back to article
Zhu, H.-N., C. Rubio-González. On the Reproducibility of Software Defect Datasets. – In: Proc. of ICSE’23, 2023, pp. 2324-2335.
Search in Google Scholar Back to article
Tufano, M., C. Watson, G. Bavota, M. diPenta, M. White, D. Poshyvanyk. An Empirical Study on Learning Bug-Fixing Patches in the Wild via Neural Machine Translation. – ACM Trans. Software Eng. Methodol., Vol. 28, 2019, No 4, pp. 1-29.
Search in Google Scholar Back to article
Renzullo, J., P. Reiter, W. Weimer, S. Forrest. Automated Program Repair: Emerging Trends Pose and Expose Problems for Benchmarks. – ACM Comput. Surv., Vol. 57, 2025, No 8, pp. 1-18.
Search in Google Scholar Back to article
Vaithilingam, P., T. Zhang, E. L. Glassman. Expectation vs Experience: Evaluating the Usability of Code Generation Tools Powered by Large Language Models. – In: Proc. of CHI EA, 2022, pp. 1-7.
Search in Google Scholar Back to article
Ouyang, Y., J. Yang, L. Zhang. Benchmarking Automated Program Repair: An Extensive Study on Both Real-World and Artificial Bugs. – In: Proc. of ISSTA’24, 2024, pp. 440-452.
Search in Google Scholar Back to article
Ahmed, I., etal. Artificial Intelligence for Software Engineering: The Journey so far and the Road Ahead. – ACM Trans. Softw. Eng. Methodol., Vol. 34, 2025, No 5, pp. 1-27.
Search in Google Scholar Back to article
Khati, D., Y. Liu, D. N. Palacio, Y. Zhang, D. Poshyvanyk. Mapping the Trust Terrain: LLMs in Software Engineering – Insights and Perspectives. – ACM Trans. Software Eng. Methodol., 2025.
Search in Google Scholar Back to article
Yang, D., Y. Lei, X. Mao, Y. Qi, X. Yi. Seeing the Whole Elephant: Systematically Understanding and Uncovering Evaluation Biases in Automated Program Repair. – ACM Trans. Software Eng. Methodol., Vol. 32, 2023, No 3, pp. 1-37.
Search in Google Scholar Back to article
Bui, Q.-C., R. Paramitha, D.-L. Vu, F. Massacci, R. Scandariato. APR4Vul: An Empirical Study of Automatic Program Repair Techniques on Real-World Java Vulnerabilities. – Empir. Software Eng., Vol. 29, 2024, No 1, 18.
Search in Google Scholar Back to article
Eladawy, H., C. LeGoues, Y. Brun. Automated Program Repair – What Is It Good for? Not Absolutely Nothing! – In: Proc. of ICSE’24, 2024, pp. 1-13.
Search in Google Scholar Back to article
Xu, P., B. Kuang, M. Su, A. F u. Survey of Large-Language-Model-Based Automated Program Repair. – J. Comput. Res. Dev., Vol. 62, 2025, No 8, pp. 2040-2057.
Search in Google Scholar Back to article
Oshi, H., J. C. Sanchez, S. Gulwani, V. Le, G. Verbruggen, I. Radiček. Repair is Nearly Generation: Multilingual Program Repair with LLMs. – In: Proc. AAAI, Vol. 37, 2023, No 4, pp. 5131-5140.
Search in Google Scholar Back to article
Sotto-Mayor, B., M. Kalech. A Survey on Transfer Learning for Cross-Project Defect Prediction. – IEEE Access, Vol. 12, 2024, pp. 93398-93425.
Search in Google Scholar Back to article
Zemin, L., etal. An Empirical Study on the Suitability of Test-Based Patch Acceptance Criteria. – ACM Trans. Software Eng. Methodol., Vol. 34, 2025, No 3, pp. 1-20.
Search in Google Scholar Back to article