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Program synthesis has been long studied with recent approaches focused on directly using the power of Large Language Models (LLMs) to generate code.
Prow: A step toward automatic program writing
R. J. Waldinger and R. C. Lee · 1969
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Toward automatic program synthesis
Z. Manna and R. J. Waldinger · 1971
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Inferring lisp programs from examples
D. E. Shaw, W. R. Swartout, and C. C. Green · 1975
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Symbolic execution and program testing
J. C. King · 1976
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Mutation analysis of program test data
T. A. Budd · 1980
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Application of theorem proving to problem solving
C. Green · 1981
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An empirical study of the reliability of unix utilities
B. P. Miller, L. Fredriksen, and B. So · 1990
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Applying’design by contract’
B. Meyer · 1992
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A threshold of ln n for approximating set cover
U. Feige · 1998
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Differential testing for software
W. M. McKeeman · 1998
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Translation validation for an optimizing compiler
G. C. Necula · 2000
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Bleu: a method for automatic evaluation of machine translation
K. Papineni, S. Roukos, T. Ward, and W.-J. Zhu · 2002
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Empirical studies of test-suite reduction
G. Rothermel, M. J. Harrold, J. Von Ronne, and C. Hong · 2002
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Violating assumptions with fuzzing
P. Oehlert · 2005
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jsfunfuzz
M. Security · 2007
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Klee: unassisted and automatic generation of high-coverage tests for complex systems programs
C. Cadar, D. Dunbar, D. R. Engler, et al · 2008
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The elements of statistical learning: data mining, inference, and prediction
T. Hastie, R. Tibshirani, J. H. Friedman, and J. H. Friedman · 2009
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Dafny: An automatic program verifier for functional correctness
K. R. M. Leino · 2010
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Automating string processing in spreadsheets using input-output examples
S. Gulwani · 2011
Earlier work this paper cites.
Finding and understanding bugs in c compilers
X. Yang, Y. Chen, E. Eide, and J. Regehr · 2011
Earlier work this paper cites.
An empirical study of junit test-suite reduction
L. Zhang, D. Marinov, L. Zhang, and S. Khurshid · 2011
Earlier work this paper cites.
Fuzzing with code fragments
C. Holler, K. Herzig, and A. Zeller · 2012
Earlier work this paper cites.
Balancing trade-offs in test-suite reduction
A. Shi, A. Gyori, M. Gligoric, A. Zaytsev, and D. Marinov · 2014
Earlier work this paper cites.
Program-adaptive mutational fuzzing
S. K. Cha, M. Woo, and D. Brumley · 2015
Earlier work this paper cites.
Continuous fuzzing with libfuzzer and addresssanitizer
K. Serebryany · 2016
Earlier work this paper cites.
Program synthesis
S. Gulwani, O. Polozov, and R. Singh · 2017
Earlier work this paper cites.
Attention is all you need
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin · 2017
Cited alongside, same era.
Mapping language to code in programmatic context
S. Iyer, I. Konstas, A. Cheung, and L. Zettlemoyer · 2018
Cited alongside, same era.
Neural-guided deductive search for real-time program synthesis from examples
A. Kalyan, A. Mohta, O. Polozov, D. Batra, P. Jain, and S. Gulwani · 2018
Cited alongside, same era.
State of mutation testing at google
G. Petrović and M. Ivanković · 2018
Cited alongside, same era.
Spider: A large-scale human-labeled dataset for complex and cross-domain semantic parsing and text-to-sql task
T. Yu, R. Zhang, K. Yang, M. Yasunaga, D. Wang, Z. Li, J. Ma, I. Li, Q. Yao, S. Roman, et al · 2018
Cited alongside, same era.
American fuzzing lop (afl)
M. Zalewski · 2018
Cited alongside, same era.
Santacoder: don’t reach for the stars!
L. B. Allal, R. Li, D. Kocetkov, C. Mou, C. Akiki, C. M. Ferrandis, N. Muennighoff, M. Mishra, A. Gu, M. Dey, et al · 2023
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Multipl-e: A scalable and polyglot approach to benchmarking neural code generation
F. Cassano, J. Gouwar, D. Nguyen, S. Nguyen, L. Phipps-Costin, D. Pinckney, M.-H. Yee, Y. Zi, C. J. Anderson, M. Q. Feldman, et al · 2023
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Vicuna: An open-source chatbot impressing gpt-4 with 90%* chatgpt quality, March 2023
W.-L. Chiang, Z. Li, Z. Lin, Y. Sheng, Z. Wu, H. Zhang, L. Zheng, S. Zhuang, Y. Zhuang, J. E. Gonzalez, I. Stoica, and E. P. Xing · 2023
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Starcoder
B. Code · 2023
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Large language models are zero-shot fuzzers: Fuzzing deep-learning libraries via large language models
Y. Deng, C. S. Xia, H. Peng, C. Yang, and L. Zhang · 2023
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Code coverage at google
M. Ivanković, G. Petrović, R. Just, and G. Fraser · 2019
Cited alongside, same era.
Language models are few-shot learners
T. Brown, B. Mann, N. Ryder, M. Subbiah, J. D. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, et al · 2020
Cited alongside, same era.
Unsupervised translation of programming languages
M.-A. Lachaux, B. Roziere, L. Chanussot, and G. Lample · 2020
Cited alongside, same era.
On the unusual effectiveness of type-aware operator mutations for testing smt solvers
D. Winterer, C. Zhang, and Z. Su · 2020
Cited alongside, same era.
Program synthesis with large language models, 2021
J. Austin, A. Odena, M. Nye, M. Bosma, H. Michalewski, D. Dohan, E. Jiang, C. Cai, M. Terry, Q. Le, and C. Sutton · 2021
Cited alongside, same era.
GPT-Neo: Large Scale Autoregressive Language Modeling with Mesh-Tensorflow, Mar. 2021
S. Black, L. Gao, P. Wang, C. Leahy, and S. Biderman · 2021
Cited alongside, same era.
Incoder: A generative model for code infilling and synthesis
D. Fried, A. Aghajanyan, J. Lin, S. Wang, E. Wallace, F. Shi, R. Zhong, S. Yih, L. Zettlemoyer, and M. Lewis · 2023
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A. Q. Jiang, A. Sablayrolles, A. Mensch, C. Bamford, D. S. Chaplot, D. d. l. Casas, F. Bressand, G. Lengyel, G. Lample, L. Saulnier, et al · 2023
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Impact of code language models on automated program repair
N. Jiang, K. Liu, T. Lutellier, and L. Tan · 2023
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Swe-bench: Can language models resolve real-world github issues?
C. E. Jimenez, J. Yang, A. Wettig, S. Yao, K. Pei, O. Press, and K. Narasimhan · 2023
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Fill in the blank: Context-aware automated text input generation for mobile gui testing
Z. Liu, C. Chen, J. Wang, X. Che, Y. Huang, J. Hu, and Q. Wang · 2023
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Wizardcoder: Empowering code large language models with evol-instruct
Z. Luo, C. Xu, P. Zhao, Q. Sun, X. Geng, W. Hu, C. Tao, J. Ma, Q. Lin, and D. Jiang · 2023
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GitHub Copilot – Your AI pair programmer
Microsoft · 2023
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Codegen2: Lessons for training llms on programming and natural languages
E. Nijkamp, H. Hayashi, C. Xiong, S. Savarese, and Y. Zhou · 2023
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Codegen: An open large language model for code with multi-turn program synthesis
E. Nijkamp, B. Pang, H. Hayashi, L. Tu, H. Wang, Y. Zhou, S. Savarese, and C. Xiong · 2023
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OpenAI · 2023
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Phind/phind-codellama-34b-v2 · hugging face
Phind · 2023
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Code llama: Open foundation models for code
B. Rozière, J. Gehring, F. Gloeckle, S. Sootla, I. Gat, X. E. Tan, Y. Adi, J. Liu, T. Remez, J. Rapin, et al · 2023
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Stablelm: Stability ai language models
Stability-AI · 2023
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Codet5+: Open code large language models for code understanding and generation
Y. Wang, H. Le, A. D. Gotmare, N. D. Bui, J. Li, and S. C. Hoi · 2023
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Y. Wei, C. S. Xia, and L. Zhang · 2023
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Automated program repair in the era of large pre-trained language models
C. S. Xia, Y. Wei, and L. Zhang · 2023
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White-box compiler fuzzing empowered by large language models, 2023
C. Yang, Y. Deng, R. Lu, J. Yao, J. Liu, R. Jabbarvand, and L. Zhang · 2023
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Codegeex: A pre-trained model for code generation with multilingual evaluations on humaneval-x
Q. Zheng, X. Xia, X. Zou, Y. Dong, S. Wang, Y. Xue, Z. Wang, L. Shen, A. Wang, Y. Li, et al · 2023
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Large language models are edge-case fuzzers: Testing deep learning libraries via fuzzgpt
Y. Deng, C. S. Xia, C. Yang, S. D. Zhang, S. Yang, and L. Zhang · 2024
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Universal fuzzing via large language models
C. S. Xia, M. Paltenghi, J. L. Tian, M. Pradel, and L. Zhang · 2024
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