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The task of generating code solutions for a given programming problem can benefit from the use of pre-trained language models such as Codex, which can produce multiple diverse samples.
Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography
Martin A Fischler and Robert C Bolles · 1981
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Feedback-directed random test generation
Carlos Pacheco, Shuvendu K Lahiri, Michael D Ernst, and Thomas Ball · 2007
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EvoSuite: automatic test suite generation for object-oriented software
Gordon Fraser and Andrea Arcuri · 2011
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Reformulating branch coverage as a many-objective optimization problem
Annibale Panichella, Fitsum Meshesha Kifetew, and Paolo Tonella · 2015
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Many independent objective (mio) algorithm for test suite generation
Andrea Arcuri · 2017
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Automated test case generation as a many-objective optimisation problem with dynamic selection of the targets
Annibale Panichella, Fitsum Meshesha Kifetew, and Paolo Tonella · 2017
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Execution-guided neural program synthesis
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Write, execute, assess: Program synthesis with a repl
Kevin Ellis, Maxwell Nye, Yewen Pu, Felix Sosa, Josh Tenenbaum, and Armando Solar-Lezama · 2019
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Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov, and Luke Zettlemoyer · 2019
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Huggingface’s transformers: State-of-the-art natural language processing
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Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
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Jacob Austin, Augustus Odena, Maxwell Nye, Maarten Bosma, Henryk Michalewski, David Dohan, Ellen Jiang, Carrie Cai, Michael Terry, Quoc Le, et al · 2021
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Gpt-neo: Large scale autoregressive language modeling with mesh-tensorflow
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Fault-aware neural code rankers
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Interactive code generation via test-driven user-intent formalization, 2022
Shuvendu K. Lahiri, Aaditya Naik, Georgios Sakkas, Piali Choudhury, Curtis von Veh, Madanlal Musuvathi, Jeevana Priya Inala, Chenglong Wang, and Jianfeng Gao · 2022
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Coderl: Mastering code generation through pretrained models and deep reinforcement learning
Hung Le, Yue Wang, Akhilesh Deepak Gotmare, Silvio Savarese, and Steven CH Hoi · 2022
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Evaluating large language models trained on code
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Leveraging automated unit tests for unsupervised code translation
Baptiste Roziere, Jie M Zhang, Francois Charton, Mark Harman, Gabriel Synnaeve, and Guillaume Lample · 2021
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Incoder: A generative model for code infilling and synthesis
Daniel Fried, Armen Aghajanyan, Jessy Lin, Sida Wang, Eric Wallace, Freda Shi, Ruiqi Zhong, Wen tau Yih, Luke Zettlemoyer, and Mike Lewis
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Incoder: A generative model for code infilling and synthesis
Daniel Fried, Armen Aghajanyan, Jessy Lin, Sida Wang, Eric Wallace, Freda Shi, Ruiqi Zhong, Wen-tau Yih, Luke Zettlemoyer, and Mike Lewis
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A conversational paradigm for program synthesis
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A systematic evaluation of large language models of code
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