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Crafting high-quality fuzz drivers not only is time-consuming but also requires a deep understanding of the library.
Language models are few-shot learners
Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., Neelakantan, A., Shyam, P., et al · 1901
Earlier work this paper cites.
Addresssanitizer: A fast address sanity checker
Serebryany, K., Bruening, D., Potapenko, A., and Vyukov, D · 2012
Earlier work this paper cites.
Coverage-based greybox fuzzing as markov chain
Böhme, M., Pham, V.-T., and Roychoudhury, A · 2016
Earlier work this paper cites.
Contract-based program repair without the contracts
Chen, L., Pei, Y., and Furia, C. A · 2017
Earlier work this paper cites.
OSS-Fuzz-google’s continuous fuzzing service for open source software
Serebryany, K · 2017
Earlier work this paper cites.
A systematic evaluation of static api-misuse detectors
Amann, S., Nguyen, H. A., Nadi, S., Nguyen, T. N., and Mezini, M · 2018
Earlier work this paper cites.
Angora: efficient fuzzing by principled search
Chen, P., and Chen, H · 2018
Earlier work this paper cites.
Shaping program repair space with existing patches and similar code
Jiang, J., Xiong, Y., Zhang, H., Gao, Q., and Chen, X · 2018
Earlier work this paper cites.
Fudge: fuzz driver generation at scale
Babić, D., Bucur, S., Chen, Y., Ivančić, F., King, T., Kusano, M., Lemieux, C., Szekeres, L., and Wang, W · 2019
Earlier work this paper cites.
Incorporating external knowledge into machine reading for generative question answering
Bi, B., Wu, C., Yan, M., Wang, W., Xia, J., and Li, C · 2019
Earlier work this paper cites.
Matryoshka: Fuzzing deeply nested branches
Chen, P., Liu, J., and Chen, H · 2019
Earlier work this paper cites.
Effective and efficient api misuse detection via exception propagation and search-based testing
Kechagia, M., Devroey, X., Panichella, A., Gousios, G., and van Deursen, A · 2019
Earlier work this paper cites.
Avatar: Fixing semantic bugs with fix patterns of static analysis violations
Liu, K., Koyuncu, A., Kim, D., and Bissyandè, T. F · 2019
Earlier work this paper cites.
Language models are unsupervised multitask learners
Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al · 2019
Earlier work this paper cites.
Superion: Grammar-aware greybox fuzzing
Wang, J., Chen, B., Wei, L., and Liu, Y · 2019
Earlier work this paper cites.
Exposing library api misuses via mutation analysis
Wen, M., Liu, Y., Wu, R., Xie, X., Cheung, S.-C., and Su, Z · 2019
Earlier work this paper cites.
Atlidakis, V., Geambasu, R., Godefroid, P., Polishchuk, M., and Ray, B · 2020
Earlier work this paper cites.
FuzzGen: Automatic fuzzer generation
Ispoglou, K., Austin, D., Mohan, V., and Payer, M · 2020
Earlier work this paper cites.
Retrieval-augmented generation for knowledge-intensive nlp tasks
Lewis, P., Perez, E., Piktus, A., Petroni, F., Karpukhin, V., Goyal, N., Küttler, H., Lewis, M., Yih, W.-t., Rocktäschel, T., et al · 2020
Earlier work this paper cites.
Rtfm! automatic assumption discovery and verification derivation from library document for api misuse detection
Lv, T., Li, R., Yang, Y., Chen, K., Liao, X., Wang, X., Hu, P., and Xing, L · 2020
Earlier work this paper cites.
Cooperative api misuse detection using correction rules
Nielebock, S., Heumüller, R., Krüger, J., and Ortmeier, F · 2020
Cited alongside, same era.
Rulf: Rust library fuzzing via api dependency graph traversal
Jiang, J., Xu, H., and Zhou, Y · 2021
Cited alongside, same era.
Winnie: Fuzzing windows applications with harness synthesis and fast cloning
Jung, J., Tong, S., Hu, H., Lim, J., Jin, Y., and Kim, T · 2021
Cited alongside, same era.
Active learning of discriminative subgraph patterns for api misuse detection
Kang, H. J., and Lo, D · 2021
Cited alongside, same era.
Sparrowhawk: Memory safety flaw detection via data-driven source code annotation
Lyu, Y., Gao, W., Ma, S., Sun, Q., and Li, J · 2021
Cited alongside, same era.
Ernie 3.0: Large-scale knowledge enhanced pre-training for language understanding and generation
Taking the next step: Oss-fuzz in 2023
Chang, O · 2023
Closest in time.
Hopper: Interpretative fuzzing for libraries
Chen, P., Xie, Y., Lyu, Y., Wang, Y., and Chen, H · 2023
Closest in time.
Large language models are zero-shot fuzzers: Fuzzing deep-learning libraries via large language models
Deng, Y., Xia, C. S., Peng, H., Yang, C., and Zhang, L · 2023
Closest in time.
Survey of hallucination in natural language generation
Ji, Z., Lee, N., Frieske, R., Yu, T., Su, D., Xu, Y., Ishii, E., Bang, Y. J., Madotto, A., and Fung, P · 2023
Closest in time.
Inferfix: End-to-end program repair with llms
Jin, M., Shahriar, S., Tufano, M., Shi, X., Lu, S., Sundaresan, N., and Svyatkovskiy, A · 2023
Closest in time.
Codamosa: Escaping coverage plateaus in test generation with pre-trained large language models
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Sun, Y., Wang, S., Feng, S., et al · 2021
Cited alongside, same era.
Mining api constraints from library and client to detect api misuses
Zeng, H., Chen, J., Shen, B., and Zhong, H · 2021
Cited alongside, same era.
APICraft: Fuzz driver generation for closed-source SDK libraries
Zhang, C., Lin, X., Li, Y., Xue, Y., Xie, J., Chen, H., Ying, X., Wang, J., and Liu, Y · 2021
Cited alongside, same era.
Intelligen: Automatic driver synthesis for fuzz testing
Zhang, M., Liu, J., Ma, F., Zhang, H., and Jiang, Y · 2021
Cited alongside, same era.
Graphfuzz: Library api fuzzing with lifetime-aware dataflow graphs
Green, H., and Avgerinos, T · 2022
Cited alongside, same era.
Utopia: Automatic generation of fuzz driver using unit tests
Jeong, B., Jang, J., Yi, H., Moon, J., Kim, J., Jeon, I., Kim, T., Shim, W., and Hwang, Y. H · 2022
Cited alongside, same era.
Large language models are zero-shot reasoners
Kojima, T., Gu, S. S., Reid, M., Matsuo, Y., and Iwasawa, Y · 2022
Cited alongside, same era.
Lemieux, C., Inala, J. P., Lahiri, S. K., and Sen, S · 2023
Closest in time.
Liu, J., Xia, C. S., Wang, Y., and Zhang, L · 2023
Closest in time.
OpenAI · 2023
Closest in time.
Peng, B., Galley, M., He, P., Cheng, H., Xie, Y., Hu, Y., Huang, Q., Liden, L., Yu, Z., Chen, W., and Gao, J · 2023
Closest in time.
Lost at c: A user study on the security implications of large language model code assistants
Sandoval, G., Pearce, H., Nys, T., Karri, R., Garg, S., and Dolan-Gavitt, B · 2023
Closest in time.
Llama 2: Open foundation and fine-tuned chat models
Touvron, H., Martin, L., Stone, K., et al · 2023
Closest in time.
Carpetfuzz: Automatic program option constraint extraction from documentation for fuzzing
Wang, D., Li, Y., Zhang, Z., and Chen, K · 2023
Closest in time.
Universal fuzzing via large language models
Xia, C. S., Paltenghi, M., Tian, J. L., Pradel, M., and Zhang, L · 2023
Closest in time.
Automated program repair in the era of large pre-trained language models
Xia, C. S., Wei, Y., and Zhang, L · 2023
Closest in time.
Conversational automated program repair
Xia, C. S., and Zhang, L · 2023
Closest in time.
Understanding large language model based fuzz driver generation
Zhang, C., Bai, M., Zheng, Y., Li, Y., Xie, X., Li, Y., Ma, W., Sun, L., and Liu, Y · 2023
Closest in time.
Understanding programs by exploiting (fuzzing) test cases
Zhao, J., Rong, Y., Guo, Y., He, Y., and Chen, H · 2023
Closest in time.
Large language models are edge-case generators: Crafting unusual programs for fuzzing deep learning libraries
Deng, Y., Xia, C., Yang, C., Zhang, S., Yang, S., and Zhang, L · 2024
Closest in time.
Afgen: Whole-function fuzzing for applications and libraries
Liu, Y., Wang, Y., Bao, T., Jia, X., Zhang, Z., and Su, P · 2024
Closest in time.