Fuzz4ALL: Universal Fuzzing with Large Language Models

BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension

Lewis, M., Liu, Y., Goyal, N., Ghazvininejad, M., Mohamed, A., Levy, O., Stoyanov, V., & Zettlemoyer, L. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2020.acl-main.703 · Cited by 8,821 · see citing works

Training language models to follow instructions with human feedback

Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C. L., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al. (2022) · arXiv · doi:10.48550/arxiv.2203.02155 · Cited by 4,208 · see citing works

CodeBERT: A Pre-Trained Model for Programming and Natural Languages

Feng, Z., Guo, D., Tang, D., Duan, N., Feng, X., Gong, M., Shou, L., Qin, B., Liu, T., Jiang, D., et al. (2020) · Findings of the Association for Computational Linguistics: EMNLP 2020 · doi:10.18653/v1/2020.findings-emnlp.139 · Cited by 2,824 · see citing works

Prefix-Tuning: Optimizing Continuous Prompts for Generation

Li, X. L., & Liang, P. (2021) · Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) · doi:10.18653/v1/2021.acl-long.353 · Cited by 2,473 · see citing works

Proceedings of the Conference on Empirical Methods in Natural Language Processing - EMNLP '08

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Exploiting Cloze-Questions for Few-Shot Text Classification and Natural Language Inference

Schick, T., & Schütze, H. (2021) · Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume · doi:10.18653/v1/2021.eacl-main.20 · Cited by 1,305 · see citing works

Finding and understanding bugs in C compilers

Yang, X., Chen, Y., Eide, E., & Regehr, J. (2011) · Proceedings of the 32nd ACM SIGPLAN Conference on Programming Language Design and Implementation · doi:10.1145/1993498.1993532 · Cited by 825 · see citing works

A Multitask, Multilingual, Multimodal Evaluation of ChatGPT on Reasoning, Hallucination, and Interactivity

Bang, Y., Cahyawijaya, S., Lee, N., Dai, W., Su, D., Wilie, B., Lovenia, H., Ji, Z., Yu, T., Chung, W., et al. (2023) · Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2023.ijcnlp-main.45 · Cited by 682 · see citing works

Evaluating Fuzz Testing

Klees, G., Ruef, A., Cooper, B., Wei, S., & Hicks, M. (2018) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/3243734.3243804 · Cited by 659 · see citing works

Fuzzing: Brute Force Vulnerability Discovery

Sutton, M. J. D., Greene, A., & Amini, P. (2007) · Medical Entomology and Zoology · Cited by 545 · see citing works

A systematic evaluation of large language models of code

Xu, F. F., Alon, U., Neubig, G., & Hellendoorn, V. J. (2022) · Proceedings of the 6th ACM SIGPLAN International Symposium on Machine Programming · doi:10.1145/3520312.3534862 · Cited by 540 · see citing works

A Survey on Automated Fact-Checking

Guo, Z., Schlichtkrull, M., & Vlachos, A. (2022) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00454 · Cited by 500 · see citing works

Learn&Fuzz: Machine learning for input fuzzing

Godefroid, P., Peleg, H., & Singh, R. (2017) · IEEE/ACM International Conference on Automated Software Engineering (ASE) · doi:10.1109/ase.2017.8115618 · Cited by 375 · see citing works

Program Synthesis

Gulwani, S., Polozov, O., & Singh, R. (2017) · Foundations and Trends® in Programming Languages · doi:10.1561/2500000010 · Cited by 357 · see citing works

Learning How to Ask: Querying LMs with Mixtures of Soft Prompts

Qin, G., & Eisner, J. (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.410 · Cited by 342 · see citing works

Large Language Models Are Zero-Shot Fuzzers: Fuzzing Deep-Learning Libraries via Large Language Models

Deng, Y., Xia, C. S., Peng, H., Yang, C., & Zhang, L. (2023) · Proceedings of the 32nd ACM SIGSOFT International Symposium on Software Testing and Analysis · doi:10.1145/3597926.3598067 · Cited by 326 · see citing works

A Survey of Compiler Testing

Chen, J., Patra, J., Pradel, M., Xiong, Y., Zhang, H., Hao, D., & Zhang, L. (2020) · ACM Computing Surveys · doi:10.1145/3363562 · Cited by 222 · see citing works

Open source software in quantum computing

Fingerhuth, M., Babej, T., & Wittek, P. (2018) · PLoS ONE · doi:10.1371/journal.pone.0208561 · Cited by 188 · see citing works

Fuzzing: Challenges and Reflections

Böhme, M., Cadar, C., & Roychoudhury, A. (2020) · IEEE Software · doi:10.1109/ms.2020.3016773 · Cited by 161 · see citing works

Compiler fuzzing through deep learning

Cummins, C., Petoumenos, P., Murray, A. C., & Leather, H. (2018) · ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA) · doi:10.1145/3213846.3213848 · Cited by 157 · see citing works

No more fine-tuning? an experimental evaluation of prompt tuning in code intelligence

Wang, C., Yang, Y., Gao, C., Peng, Y. F., Zhang, H., & Lyu, M. R.-T. (2022) · ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE) · doi:10.1145/3540250.3549113 · Cited by 153 · see citing works

Random testing for C and C++ compilers with YARPGen

Livinskii, V., Babokin, D., & Regehr, J. D. (2020) · Proceedings of the ACM on Programming Languages · doi:10.1145/3428264 · Cited by 144 · see citing works

Free lunch for testing: fuzzing deep-learning libraries from open source

Wei, A., Deng, Y., Yang, C., & Zhang, L. (2022) · Proceedings of the 44th International Conference on Software Engineering · doi:10.1145/3510003.3510041 · Cited by 106 · see citing works

On the reliability of coverage-based fuzzer benchmarking

Böhme, M., Szekeres, L., & Metzman, J. (2022) · Proceedings of the 44th International Conference on Software Engineering · doi:10.1145/3510003.3510230 · Cited by 104 · see citing works

DeepFuzz: Automatic Generation of Syntax Valid C Programs for Fuzz Testing

Liu, X., Li, X., Prajapati, R., & Wu, D. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33011044 · Cited by 101 · see citing works

NNSmith: Generating Diverse and Valid Test Cases for Deep Learning Compilers

Liu, J., Lin, J., Ruffy, F., Tan, C., Li, J., Panda, A., & Zhang, L. (2023) · International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS) · doi:10.1145/3575693.3575707 · Cited by 99 · see citing works

Validating SMT solvers via semantic fusion

Winterer, D., Zhang, C., & Su, Z. (2020) · Proceedings of the 41st ACM SIGPLAN Conference on Programming Language Design and Implementation · doi:10.1145/3385412.3385985 · Cited by 82 · see citing works

GrayC: Greybox Fuzzing of Compilers and Analysers for C

Even-Mendoza, K., Sharma, A., Donaldson, A. F., & Cadar, C. (2023) · ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA) · doi:10.1145/3597926.3598130 · Cited by 74 · see citing works

SeqFuzzer: An Industrial Protocol Fuzzing Framework from a Deep Learning Perspective

Zhao, H., Li, Z., Wei, H., Shi, J., & Huang, Y. (2019) · IEEE International Conference on Software Testing, Verification and Validation (ICST) · doi:10.1109/icst.2019.00016 · Cited by 73 · see citing works

Bugs in Quantum computing platforms: an empirical study

Paltenghi, M., & Pradel, M. (2022) · Proceedings of the ACM on Programming Languages · doi:10.1145/3527330 · Cited by 73 · see citing works

Many-core compiler fuzzing

Lidbury, C., Lascu, A., Chong, N., & Donaldson, A. F. (2015) · ACM SIGPLAN Notices · doi:10.1145/2813885.2737986 · Cited by 66 · see citing works

History-driven test program synthesis for JVM testing

Zhao, Y., Wang, Z., Chen, J., Liu, M., Wu, M., Zhang, Y., & Zhang, L. (2022) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1145/3510003.3510059 · Cited by 61 · see citing works

MorphQ: Metamorphic Testing of the Qiskit Quantum Computing Platform

Paltenghi, M., & Pradel, M. (2023) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1109/icse48619.2023.00202 · Cited by 57 · see citing works

On the unusual effectiveness of type-aware operator mutations for testing SMT solvers

Winterer, D., Zhang, C., & Su, Z. (2020) · Proceedings of the ACM on Programming Languages · doi:10.1145/3428261 · Cited by 56 · see citing works

One Engine to Fuzz ’em All: Generic Language Processor Testing with Semantic Validation

Chen, Y., Zhong, R., Hu, H. P., Zhang, H., Yang, Y., Wu, D., & Lee, W. (2021) · IEEE Symposium on Security and Privacy (S&P) · doi:10.1109/sp40001.2021.00071 · Cited by 55 · see citing works

Coverage-guided tensor compiler fuzzing with joint IR-pass mutation

Liu, J., Wei, Y., Yang, S., Deng, Y., & Zhang, L. (2022) · Proceedings of the ACM on Programming Languages · doi:10.1145/3527317 · Cited by 53 · see citing works

Finding typing compiler bugs

Chaliasos, S., Sotiropoulos, T., Spinellis, D. D., Gervais, A., Livshits, B., & Mitropoulos, D. (2022) · ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI) · doi:10.1145/3519939.3523427 · Cited by 37 · see citing works

Generative type-aware mutation for testing SMT solvers

Park, J., Winterer, D., Zhang, C., & Su, Z. (2021) · Proceedings of the ACM on Programming Languages · doi:10.1145/3485529 · Cited by 33 · see citing works

Code Generation Tools (Almost) for Free? A Study of Few-Shot, Pre-Trained Language Models on CodePreprint

Bareiß, P., Souza, B., d’Amorim, M., & Pradel, M. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2206.01335 · Cited by 31 · see citing works

No Grammar, No Problem: Towards Fuzzing the Linux Kernel without System-Call Descriptions

Bulekov, A., Das, B., Hajnoczi, S., & Egele, M. (2023) · Network and Distributed System Security Symposium · doi:10.14722/ndss.2023.24688 · Cited by 31 · see citing works

CsmithEdge: more effective compiler testing by handling undefined behaviour less conservatively

Even-Mendoza, K., Cadar, C., & Donaldson, A. F. (2022) · Empirical Software Engineering · doi:10.1007/s10664-022-10146-1 · Cited by 29 · see citing works

TF-Coder: Program Synthesis for Tensor Manipulations

Shi, K., Bieber, D., & Singh, R. (2022) · ACM Transactions on Programming Languages and Systems · doi:10.1145/3517034 · Cited by 27 · see citing works

Metamorphic testing of Datalog engines

Mansur, M. N., Christakis, M., & Wüstholz, V. (2021) · ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE) · doi:10.1145/3468264.3468573 · Cited by 26 · see citing works

Making no-fuss compiler fuzzing effective

Groce, A., van Tonder, R., Kalburgi, G. T., & Goues, C. L. (2022) · ACM SIGPLAN International Conference on Compiler Construction (CC) · doi:10.1145/3497776.3517765 · Cited by 16 · see citing works

Feedback-Directed Differential Testing of Interactive Debuggers (Artifact)

Lehmann, D., & Pradel, M. (2018) · Artifact Digital Object Group · doi:10.1145/3277000 · Cited by 5 · see citing works

LLM-Based Multi-Agent Systems for Software Engineering: Literature Review, Vision, and the Road Ahead

He, J., Treude, C., & Lo, D. (2025) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3712003 · Cited by 246 · see citing works

When LLMs meet cybersecurity: a systematic literature review

Zhang, J., Bu, H., Wen, H., Liu, Y., Fei, H., Xi, R., Li, L., Yang, Y., Zhu, H., & Meng, D. (2025) · Cybersecurity · doi:10.1186/s42400-025-00361-w · Cited by 220 · see citing works

Enhancing Autonomous System Security and Resilience With Generative AI: A Comprehensive Survey

Lopez, M. A., Lunardi, W. T., Lawton, G., & Thakkar, S. (2024) · IEEE Access · doi:10.1109/access.2024.3439363 · Cited by 115 · see citing works

Demystifying LLM-Based Software Engineering Agents

Xia, C. S., Deng, Y., Dunn, S., & Zhang, L. (2025) · Proceedings of the ACM on Software Engineering · doi:10.1145/3715754 · Cited by 87 · see citing works

Generative AI for self-adaptive systems: State of the art and research roadmap

Li, J., Zhang, M., Li, N., Weyns, D., Jin, Z., & Tei, K. (2024) · ACM Transactions on Autonomous and Adaptive Systems · doi:10.1145/3686803 · Cited by 77 · see citing works

LLMIF: Augmented Large Language Model for Fuzzing IoT Devices

Wang, J., Yu, L., & Luo, X. (2024) · IEEE Symposium on Security and Privacy (S&P) · doi:10.1109/sp54263.2024.00211 · Cited by 68 · see citing works

SpecGen: Automated Generation of Formal Program Specifications via Large Language Models

Ma, L., Liu, S., Li, Y., Xie, X., & Bu, L. (2025) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1109/icse55347.2025.00129 · Cited by 98 · see citing works

CoverUp: Effective High Coverage Test Generation for Python

Pizzorno, J. A., & Berger, E. D. (2025) · Proceedings of the ACM on software engineering. · doi:10.1145/3729398 · Cited by 84 · see citing works

SWE-bench: Can Language Models Resolve Real-World GitHub Issues?

Jimenez, C. E., Yang, J., Wettig, A., Yao, S., Pei, K., Press, O., & Narasimhan, K. (2023) · arXiv · doi:10.48550/arxiv.2310.06770 · Cited by 72 · see citing works

Can LLMs Replace Manual Annotation of Software Engineering Artifacts?

Ahmed, T., Dévanbu, P., Treude, C., & Pradel, M. (2025) · International Conference on Mining Software Repositories (MSR) · doi:10.1109/msr66628.2025.00086 · Cited by 41 · see citing works

Knowledge Transfer from High-Resource to Low-Resource Programming Languages for Code LLMs

Cassano, F., Gouwar, J., Lucchetti, F., Schlesinger, C., Freeman, A., Anderson, C. J., Feldman, M. Q., Greenberg, M., Jangda, A., & Guha, A. (2024) · Proceedings of the ACM on Programming Languages · doi:10.1145/3689735 · Cited by 39 · see citing works

You Name It, I Run It: An LLM Agent to Execute Tests of Arbitrary Projects

Bouzenia, I., & Pradel, M. (2025) · Proceedings of the ACM on Software Engineering · doi:10.1145/3728922 · Cited by 35 · see citing works

SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering

Yang, J., Jimenez, C. E., Wettig, A., Lieret, K., Yao, S., Narasimhan, K., & Press, O. (2024) · arXiv · doi:10.48550/arxiv.2405.15793 · Cited by 31 · see citing works

The Evolution of Large Language Model: Models, Applications and Challenges

Sindhu, B., Prathamesh, R. P., Sameera, M. B., & Kumaraswamy, S. (2024) · International Conference on Current Trends in Advanced Computing (ICCTAC) · doi:10.1109/icctac61556.2024.10581180 · Cited by 34 · see citing works

When Large Language Models Meet Evolutionary Algorithms: Potential Enhancements and Challenges

Wang, C., Zhao, J., Jiao, L., Li, L., Liu, F., & Yang, S. (2025) · Research · doi:10.34133/research.0646 · Cited by 38 · see citing works

Analyzing Quantum Programs with LintQ: A Static Analysis Framework for Qiskit

Paltenghi, M., & Pradel, M. (2024) · Proceedings of the ACM on software engineering. · doi:10.1145/3660802 · Cited by 29 · see citing works

Beyond Random Inputs: A Novel ML-Based Hardware Fuzzing

Rostami, M., Chilese, M., Zeitouni, S., Kande, R., Rajendran, J., & Sadeghi, A. (2024) · Design, Automation, and Test in Europe · doi:10.23919/date58400.2024.10546625 · Cited by 28 · see citing works

The Mutators Reloaded: Fuzzing Compilers with Large Language Model Generated Mutation Operators

Ou, X., Li, C., Jiang, Y., & Xu, C. (2024) · International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS) · doi:10.1145/3622781.3674171 · Cited by 28 · see citing works

SecureQwen: Leveraging LLMs for vulnerability detection in python codebases

Mechri, A., Ferrag, M. A., & Debbah, M. A. (2024) · Computers & Security · doi:10.1016/j.cose.2024.104151 · Cited by 27 · see citing works

Enhancing Genetic Improvement Mutations Using Large Language Models

Brownlee, A. E. I., Callan, J., Even-Mendoza, K., Geiger, A., Hanna, C., Petke, J., Sarro, F., & Sobania, D. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-48796-5_13 · Cited by 24 · see citing works

ProphetFuzz: Fully Automated Prediction and Fuzzing of High-Risk Option Combinations with Only Documentation via Large Language Model

Wang, D., Zhou, G., Chen, L., Li, D., & Miao, Y. (2024) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/3658644.3690231 · Cited by 23 · see citing works

ChatHTTPFuzz: large language model-assisted IoT HTTP fuzzing

Yang, Z., Hao, P., Jiang, Y., Li, X., Du, H., Wang, S., & Liu, J. (2025) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-024-02527-3 · Cited by 22 · see citing works

An empirical evaluation of pre-trained large language models for repairing declarative formal specifications

Alhanahnah, M., Hasan, M. R., Xu, L., & Bagheri, H. (2025) · Empirical Software Engineering · doi:10.1007/s10664-025-10687-1 · Cited by 20 · see citing works

Towards Understanding the Effectiveness of Large Language Models on Directed Test Input Generation

Jiang, Z., Wen, M., Cao, J., Shi, X., & Jin, H. (2024) · IEEE/ACM International Conference on Automated Software Engineering (ASE) · doi:10.1145/3691620.3695513 · Cited by 22 · see citing works

AI Integration in the IT Professional Workplace: A Scoping Review and Interview Study with Implications for Education and Professional Competencies

Clear, T., Cajander, Å., Clear, A., McDermott, R., Daniels, M., Divitini, M., Forshaw, M., Humble, N., Kasinidou, M., Kleanthous, S., et al. (2025) · 2024 Working Group Reports on Innovation and Technology in Computer Science Education · doi:10.1145/3689187.3709607 · Cited by 19 · see citing works

An LLM-Based Agent-Oriented Approach for Automated Code Design Issue Localization

Batole, F., O'Brien, D. P., Nguyen, T. N., Dyer, R., & Rajan, H. (2025) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1109/icse55347.2025.00100 · Cited by 20 · see citing works

Understanding Software Engineering Agents: A Study of Thought-Action-Result Trajectories

Bouzenia, I., & Pradel, M. (2025) · IEEE/ACM International Conference on Automated Software Engineering (ASE) · doi:10.1109/ase63991.2025.00234 · Cited by 19 · 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

Demonstration-Free: Towards More Practical Log Parsing with Large Language Models

Xiao, Y., Le, V.-H., & Zhang, H. (2024) · IEEE/ACM International Conference on Automated Software Engineering (ASE) · doi:10.1145/3691620.3694994 · Cited by 33 · see citing works

Alibaba LingmaAgent: Improving Automated Issue Resolution via Comprehensive Repository Exploration

Ma, Y., Yang, Q., Cao, R., Li, B., Huang, F., & Li, Y. (2025) · ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE) · doi:10.1145/3696630.3728549 · Cited by 49 · see citing works

Rug: Turbo Llm for Rust Unit Test Generation

Cheng, X. C., Sang, F., Zhai, Y., Zhang, X., & Kim, T. (2025) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1109/icse55347.2025.00097 · Cited by 17 · see citing works

CTINexus: Automatic Cyber Threat Intelligence Knowledge Graph Construction Using Large Language Models

Cheng, Y., Bajaber, O., Tsegai, S. A., Song, D. X., & Gao, P. (2025) · IEEE European Symposium on Security and Privacy · doi:10.1109/eurosp63326.2025.00057 · Cited by 25 · see citing works

Moderating New Waves of Online Hate with Chain-of-Thought Reasoning in Large Language Models

Vishwamitra, N., Guo, K., Romit, F. T., Ondracek, I., Cheng, L., Zhao, Z., & Hu, H. (2024) · IEEE Symposium on Security and Privacy (S&P) · doi:10.1109/sp54263.2024.00181 · Cited by 16 · see citing works

Unveiling the microservices testing methods, challenges, solutions, and solutions gaps: A systematic mapping study

Hui, M., Wang, L., Li, H., Ren, Y., Song, Y., Zhuang, H., Cui, D., & Li, Q. (2024) · Journal of Systems and Software · doi:10.1016/j.jss.2024.112232 · Cited by 22 · see citing works

Evaluating Large Language Models for Enhanced Fuzzing: An Analysis Framework for LLM-Driven Seed Generation

Black, G., Vaidyan, V. M., & Comert, G. (2024) · IEEE Access · doi:10.1109/access.2024.3484947 · Cited by 15 · see citing works

Adversarial generation method for smart contract fuzz testing seeds guided by chain-based LLM

Sun, J., Yin, Z., Zhang, H., Chen, X., & Wei, Z. M. (2024) · Automated Software Engineering · doi:10.1007/s10515-024-00483-4 · Cited by 15 · see citing works

On the Challenges of Fuzzing Techniques via Large Language Models

Huang, L., Zhao, P., Ma, L., & Chen, H. (2025) · IEEE International Conference on Software Services Engineering (SSE) · doi:10.1109/sse67621.2025.00028 · Cited by 18 · see citing works

Validating Network Protocol Parsers with Traceable RFC Document Interpretation

Zheng, M., Xie, D., Shi, Q., Wang, C., & Zhang, X. (2025) · Proceedings of the ACM on software engineering. · doi:10.1145/3728955 · Cited by 13 · see citing works

Clozemaster: Fuzzing Rust Compiler by Harnessing Llms for Infilling Masked Real Programs

Gao, H., Yang, Y., Sun, M., Wu, J., Zhou, Y., & Xu, B. (2025) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1109/icse55347.2025.00175 · Cited by 12 · see citing works

Are LLMs Correctly Integrated into Software Systems?

Shao, Y., Huang, Y., Shen, J., Ma, L., Su, T., & Wan, C. (2025) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1109/icse55347.2025.00204 · Cited by 20 · see citing works

Mokav: Execution-driven differential testing with LLMs

Etemadi, K., Mohammadi, B., Su, Z., & Monperrus, M. (2025) · Journal of Systems and Software · doi:10.1016/j.jss.2025.112571 · Cited by 11 · see citing works

Large Language Models Can Connect the Dots: Exploring Model Optimization Bugs with Domain Knowledge-Aware Prompts

Guan, H., Bai, G., & Liu, Y. (2024) · ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA) · doi:10.1145/3650212.3680383 · Cited by 10 · see citing works

Enhancing search-based testing with LLMs for finding bugs in system simulators

Dakhama, A., Even-Mendoza, K., Langdon, W. B., Menéndez, H. D., & Petke, J. (2025) · Automated Software Engineering · doi:10.1007/s10515-025-00531-7 · Cited by 10 · see citing works

Challenges and Paths Towards AI for Software EngineeringPreprint

Gu, A., Jain, N., Li, W., Shetty, M., Shao, Y., Li, Z., Yang, D., Ellis, K., Sen, K., & Solar-Lezama, A. (2025) · Qeios · doi:10.32388/vv1661 · Cited by 24 · see citing works

MR-Adopt: Automatic Deduction of Input Transformation Function for Metamorphic Testing

Xu, C., Chen, S., Wu, J., Cheung, S.-C., Terragni, V., Zhu, H., & Cao, J. (2024) · IEEE/ACM International Conference on Automated Software Engineering (ASE) · doi:10.1145/3691620.3696020 · Cited by 9 · see citing works

Large language model based mutations in genetic improvement

Brownlee, A. E. I., Callan, J., Even-Mendoza, K., Geiger, A., Hanna, C., Petke, J., Sarro, F., & Sobania, D. (2025) · Automated Software Engineering · doi:10.1007/s10515-024-00473-6 · Cited by 9 · see citing works

An MLLM-Assisted Web Crawler Approach for Web Application Fuzzing

Yang, W., Wang, E., Gui, Z., Yuan, Z., Wang, B., & Xie, W. (2025) · Applied Sciences · doi:10.3390/app15020962 · Cited by 9 · see citing works

LLM4TDG: test-driven generation of large language models based on enhanced constraint reasoning

Liu, J., Liang, R., Zhu, X., Zhang, Y., Liu, Y., & Liu, Q. (2025) · Cybersecurity · doi:10.1186/s42400-024-00335-4 · Cited by 9 · see citing works

Kitten: A Simple Yet Effective Baseline for Evaluating LLM-Based Compiler Testing Techniques

Xie, Y., Xu, Z., Tian, Y., Zhou, M. Q., Zhou, X., & Sun, C. P. (2025) · ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA) · doi:10.1145/3713081.3731731 · Cited by 9 · see citing works

FuzzCoder: Code Large Language Model-Based Fuzz Testing for Industrial IoT Programs

Yang, L., Wei, C., Yang, J., Xia, W., Yang, Y., Luo, Y., Niyato, D. T., Sun, L., & Liu, Z. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2025.3577602 · Cited by 11 · see citing works

Bringing Rust to Safety-Critical Systems in Space

Seidel, L., & Beier, J. (2024) · doi:10.23919/3s60530.2024.10592287 · Cited by 8 · see citing works

MSFuzz: Augmenting Protocol Fuzzing with Message Syntax Comprehension via Large Language Models

Cheng, M., Zhu, K.-L., Chen, Y., Yang, G., Lu, Y., & Lu, C. (2024) · Electronics · doi:10.3390/electronics13132632 · Cited by 8 · see citing works

Resource-Efficient & Effective Code Summarization

Afrin, S., Call, J., Nguyen, K.-N., Chaparro, O., & Mastropaolo, A. (2025) · IEEE/ACM International Conference on AI Foundation Models and Software Engineering (FORGE) · doi:10.1109/forge66646.2025.00032 · Cited by 11 · see citing works

Unlocking Low Frequency Syscalls in Kernel Fuzzing with Dependency-Based RAG

Zhang, Z., Li, L., Liang, R., & Chen, K. (2025) · Proceedings of the ACM on software engineering. · doi:10.1145/3728913 · Cited by 6 · see citing works

Enhancing Protocol Fuzzing via Diverse Seed Corpus Generation

Luo, Z., Du, Q., Wang, Y., Roychoudhury, A., & Jiang, Y. (2025) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2025.3595396 · Cited by 8 · see citing works

An Automated Blackbox Noncompliance Checker for QUIC Server Implementations

Ang, K. K., Farrelly, G., Pope, C., & Ranasinghe, D. C. (2025) · ACM Asia Conference on Computer and Communications Security (ASIA CCS) · doi:10.1145/3708821.3736222 · Cited by 6 · see citing works

Fuzzing C++ Compilers via Type-Driven Mutation

Wang, B., Chen, C., Deng, M., Chen, J., Zhang, X., Lin, Y., Hao, D., & Sun, J. (2025) · Proceedings of the ACM on Programming Languages · doi:10.1145/3763094 · Cited by 6 · see citing works

CodeDoctor: multi-category code review comment generation

Li, Y., Wu, Y., Wang, Z., Huang, L., Wang, J., Li, J. K., & Huang, M. (2025) · Automated Software Engineering · doi:10.1007/s10515-025-00491-y · Cited by 6 · see citing works

A survey of coverage-guided greybox fuzzing with deep neural models

Qiu, J., Jiang, Y., Miao, Y., Luo, W., Pan, L., & Zheng, X. (2025) · Information and Software Technology · doi:10.1016/j.infsof.2025.107797 · Cited by 6 · see citing works

QTRAN: Extending Metamorphic-Oracle Based Logical Bug Detection Techniques for Multiple-DBMS Dialect Support

Lin, L. G., Zhu, Q., Chen, H., Wang, Z., Wu, R., & Xie, X. (2025) · Proceedings of the ACM on software engineering. · doi:10.1145/3728908 · Cited by 5 · see citing works

Unleashing the Power of LLM to Infer State Machine From the Protocol Implementation

Wei, H., Chen, L., Du, Z., Wu, Y., Huang, H., Liu, Y., Cheng, G., Xu, F., Wang, L., & Mao, B. (2025) · International Workshop on Quality of Service · doi:10.1109/iwqos65803.2025.11143461 · Cited by 5 · see citing works

LLM-based Satisfiability Checking of String Requirements by Consistent Data and Checker Generation

Chen, B.-Q., Babikian, A. A., Feng, S., Varró, D., & Mussbacher, G. (2025) · IEEE International Requirements Engineering Conference · doi:10.1109/re63999.2025.00030 · Cited by 7 · see citing works

Interleaving Large Language Models for Compiler Testing

Ni, Y., & Li, S. (2025) · Proceedings of the ACM on Programming Languages · doi:10.1145/3763079 · Cited by 6 · see citing works

P rome F uzz : A Knowledge-Driven Approach to Fuzzing Harness Generation with Large Language Models

Liu, Y., Deng, J., Jia, X., Wang, Y., Wang, M., Huang, L., Wei, T., & Su, P. (2025) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/3719027.3765222 · Cited by 5 · see citing works

MCP-enabled agentic AI workflow for building energy modelling: framework and use cases

Li, H., Zhang, L., Zhou, H., & Hong, T. (2026) · Journal of Building Performance Simulation · doi:10.1080/19401493.2026.2653969 · Cited by 5 · see citing works

Enhancing Black-box Compiler Option Fuzzing with LLM through Command Feedback

Wang, T., Wang, R., Chen, Y., Yu, L., Pan, Z., Zhang, M., Ma, H., & Zheng, J. (2024) · Proceedings/Proceedings - International Symposium on Software Reliability Engineering · doi:10.1109/issre62328.2024.00039 · Cited by 6 · see citing works

A Holistic Review of Fuzzing for Vulnerability Assessment in Industrial Network Protocols

Reda, A. M., Moustafa, N., & Lakshika, E. (2025) · IEEE Open Journal of the Communications Society · doi:10.1109/ojcoms.2025.3569291 · Cited by 5 · see citing works

Oracle-Guided Program Selection from Large Language Models

Fan, Z., Ruan, H., Mechtaev, S., & Roychoudhury, A. (2024) · ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA) · doi:10.1145/3650212.3680308 · Cited by 5 · see citing works

Optimization for threat classification of various data types-based on ML model and LLM

Hong, C., & Oh, T. (2025) · Scientific Reports · doi:10.1038/s41598-025-05182-y · Cited by 5 · see citing works

SWE-GPT: A Process-Centric Language Model for Automated Software Improvement

Ma, Y., Cao, R., Cao, Y., Zhang, Y., Chen, J., Liu, Y., Liu, Y., Li, B., Huang, F., & Li, Y. (2025) · Proceedings of the ACM on software engineering. · doi:10.1145/3728981 · Cited by 22 · see citing works

Shaking Up Quantum Simulators with Fuzzing and Rigour

Klimis, V., Bensoussan, A., Chachkarova, E., Even-Mendoza, K., Fortz, S., & Lenihan, C. (2025) · Proceedings of the ACM on Programming Languages · doi:10.1145/3763100 · Cited by 4 · see citing works

CtxFuzz: Discovering heap-based memory vulnerabilities through context heap operation sequence guided fuzzing

Jiang, J., Wen, C., Fu, Z., & Qin, S. (2025) · Science of Computer Programming · doi:10.1016/j.scico.2025.103395 · Cited by 4 · see citing works

Bare-Metal Firmware Fuzzing: A Survey of Techniques and Approaches

Asmita, A., Tsang, R., Ghimire, S., Salehi, S., & Homayoun, H. (2025) · IEEE Access · doi:10.1109/access.2025.3575691 · Cited by 4 · see citing works

Program Interoperable Large Language Model Software Testing Scheme: A Case Study on JavaScript Engine Fuzzing

Cao, D., Hong, Y., Pan, Q., & Wu, J. (2025) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2025.3581213 · Cited by 4 · see citing works

One Mutation Fits All: Exploring Universal Library Fuzzing Based on Exogenous Mutation

Dong, R., Tong, F., Huang, H., Zhu, X., Xiao, X., Wang, S., Wen, S., & Xiang, Y. (2025) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2025.3588878 · Cited by 4 · see citing works

Search+LLM-Based Testing for ARM Simulators

Even-Mendoza, K., Menéndez, H. D., Langdon, W. B., Dakhama, A., Petke, J., & Bruce, B. R. (2025) · IEEE/ACM International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP) · doi:10.1109/icse-seip66354.2025.00047 · Cited by 4 · see citing works

Enhancing Automated Unit Test Generation with Large Language Models: A Systematic Literature Review

Zhang, J., Hu, X., Gao, C., Xia, X., & Li, S. (2026) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3802827 · Cited by 6 · see citing works

Enhancing API Security Testing Against BOLA and Authentication Vulnerabilities Through an LLM-Enhanced Framework

Pașca, E. M., Erdei, R., Delinschi, D., & Matei, O. (2024) · Lecture notes in networks and systems · doi:10.1007/978-3-031-75010-6_23 · Cited by 4 · see citing works

Large Language Model guided State Selection Approach for Fuzzing Network Protocol

Yu, B., Song, Q., & Cai, C. (2024) · IEEE International Performance, Computing, and Communications Conference · doi:10.1109/ipccc59868.2024.10850039 · Cited by 4 · see citing works

RGFuzz: Rule-Guided Fuzzer for WebAssembly Runtimes

Park, J., Kim, Y., & Yun, I. (2025) · IEEE Symposium on Security and Privacy (S&P) · doi:10.1109/sp61157.2025.00003 · Cited by 4 · see citing works

Large Language Models-Aided Program Debloating

Lin, B., Wang, S., Qin, Y., Chen, L., & Mao, X. (2025) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2025.3594673 · Cited by 5 · see citing works

Beyond pip Install: Evaluating LLM Agents for the Automated Installation of Python Projects

Milliken, L., Kang, S., & Yoo, S. (2025) · IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER) · doi:10.1109/saner64311.2025.00009 · Cited by 5 · see citing works

CrossGuard : Runtime‐Adaptive LLM Fuzzing for Cross‐Contract Vulnerabilities Detection

Ali, G. M. A., Chen, H., Wang, Z., & Du, C. (2025) · Concurrency and Computation Practice and Experience · doi:10.1002/cpe.70421 · Cited by 3 · see citing works

LLM-Guided Genetic Improvement: Envisioning Semantic Aware Automated Software Evolution

Even-Mendoza, K., Brownlee, A., Geiger, A., Hanna, C., Petke, J., Sarro, F., & Sobania, D. (2025) · IEEE/ACM International Conference on Automated Software Engineering (ASE) · doi:10.1109/ase63991.2025.00339 · Cited by 3 · see citing works

Less Is More: Failing Test Generation with Large Language Models

Li, T.-O., Yan, X., Zong, W., Wang, Y., Tian, H., Xu, C., Wang, Y., Cheung, S.-C., & Kramer, J. (2026) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3793675 · Cited by 3 · see citing works

DynER: Optimized Test Case Generation for Representational State Transfer (REST)ful Application Programming Interface (API) Fuzzers Guided by Dynamic Error Responses

Chen, J., Chen, Y., Pan, Z., Chen, Y., Li, Y., Li, Y., Zhang, M., & Shen, Y. (2024) · Electronics · doi:10.3390/electronics13173476 · Cited by 3 · see citing works

MorphQ++: A Reproducibility Study of Metamorphic Testing on Quantum Compilers

Kitt, L., & Cohen, M. B. (2024) · IEEE/ACM International Conference on Automated Software Engineering Workshops (ASEW) · doi:10.1145/3691621.3694959 · Cited by 3 · see citing works

Large Language Model-enabled Vulnerability Investigation: A Review

Pan, Z., Liu, J., Dai, Y., & Fan, W. (2024) · International Conference on Intelligent Computing and Next Generation Networks (ICNGN) · doi:10.1109/icngn63705.2024.10871716 · Cited by 3 · see citing works

A Comprehensive Study of OOP-Related Bugs in C++ Compilers

Wang, B., Chen, C., Chen, J., Xu, B., Ye, C., Lin, Y., Dong, G., & Sun, J. (2025) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2025.3566490 · Cited by 3 · see citing works

Can Large Language Models Discover Metamorphic Relations? A Large-Scale Empirical Study

Zhang, J., Sun, C., Liu, H., & Dong, S. (2025) · IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER) · doi:10.1109/saner64311.2025.00011 · Cited by 3 · see citing works

IRFuzzer: Specialized Fuzzing for LLVM Backend Code Generation

Rong, Y., Yu, Z., Weng, Z., Neuendorffer, S., & Chen, H. (2025) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1109/icse55347.2025.00130 · Cited by 3 · see citing works

Large Language Model-Powered Protected Interface Evasion: Automated Discovery of Broken Access Control Vulnerabilities in Internet of Things Devices

Wang, E., Xie, W., Li, S., Liu, R., Zhou, Y. W., Wang, Z., Ma, S., Yang, W., & Wang, B. (2025) · Sensors · doi:10.3390/s25092913 · Cited by 3 · see citing works

CRISPE: Semantic-Guided Execution Planning and Dynamic Reasoning for Enhancing Code Coverage Prediction

Dhulipala, H., Yadavally, A., Patel, S. S., & Nguyen, T. N. (2025) · Proceedings of the ACM on software engineering. · doi:10.1145/3729401 · Cited by 2 · see citing works

FreeWavm: Enhanced WebAssembly Runtime Fuzzing Guided by Parse Tree Mutation and Snapshot

Qian, P., Ying, X., Wang, J., Liu, L., Zhang, L., Chen, J., & He, Q. (2025) · Proceedings of the ACM on software engineering. · doi:10.1145/3728877 · Cited by 2 · see citing works

Treefix: Enabling Execution with a Tree of Prefixes

Souza, B., & Pradel, M. (2025) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1109/icse55347.2025.00215 · Cited by 2 · see citing works

Finding Compiler Bugs through Cross-Language Code Generator and Differential Testing

Feng, Q., Ma, X., Feng, Z., Akhin, M., Song, W., & Liang, P. (2025) · Proceedings of the ACM on Programming Languages · doi:10.1145/3763152 · Cited by 2 · see citing works

Reference-Based Retrieval-Augmented Unit Test Generation

Zhang, Z., Liu, X., Lin, Y., Gao, X., Sun, H., & Yuan, Y. (2025) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3765758 · Cited by 2 · see citing works

SATORI: Static Test Oracle Generation for REST APIs

Alonso, J. C. G., Martin-Lopez, A., Segura, S., Bavota, G., & Ruiz–Cortés, A. (2025) · IEEE/ACM International Conference on Automated Software Engineering (ASE) · doi:10.1109/ase63991.2025.00116 · Cited by 3 · see citing works

LLMs for Automated Unit Test Generation and Assessment in Java: The AgoneTest Framework

Lops, A., Narducci, F., Ragone, A., Trizio, M., & Bartolini, C. (2025) · IEEE/ACM International Conference on Automated Software Engineering (ASE) · doi:10.1109/ase63991.2025.00198 · Cited by 2 · see citing works

Once4All: Skeleton-Guided SMT Solver Fuzzing with LLM-Synthesized Generators

Sun, M., Yang, Y., & Zhou, Y. (2026) · International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS) · doi:10.1145/3779212.3790195 · Cited by 2 · see citing works

CoverFuzz: A Coverage‐Guided and General‐Purpose Fuzzing Framework

Lin, R., Wang, R., Xu, X., Long, G., & Zhang, D. (2026) · Journal of Software Evolution and Process · doi:10.1002/smr.70122

LMFuzz: Program repair fuzzing based on large language models

Lin, R., Wang, R., Hu, G., & Xu, X. (2025) · Automated Software Engineering · doi:10.1007/s10515-025-00568-8 · Cited by 2 · see citing works

FunFuzz: An LLM-Powered Evolutionary Fuzzing FrameworkPreprint

Béjar, M. R., Romera‐Paredes, B., & Hernández-Ramos, J. L. (2026) · arXiv (Cornell University) · doi:10.48550/arxiv.2605.02789

Mut4All: Fuzzing Compilers via LLM-Synthesized Mutators Learned from Bug ReportsPreprint

Wang, B., Wang, P., Chen, C., Deng, M., Shi, J., Sun, Q., Yang, C., Lin, Y., Yang, Z., Chen, J., et al. (2025) · arXiv (Cornell University) · doi:10.48550/arxiv.2507.19275 · Cited by 4 · see citing works

A Cross-Language Dynamic Vulnerability Detection Methodology Driven by Multi-Model Collaboration

Ma, H., Xie, P., Xue, P., Li, Y., & Li, Y. (2025) · IEEE Joint International Information Technology and Artificial Intelligence Conference (ITAIC) · doi:10.1109/itaic64559.2025.11163165