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Large language models (LLMs) such as ChatGPT have shown remarkable capabilities in code generation.
Language models are few-shot learners
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The Power of Scale for Parameter-Efficient Prompt Tuning. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, EMNLP 2021, Virtual Event / Punta Cana, Dominican Republic, 7-11 November, 2021 , Marie-Francine Moens, Xuanjing Huang, Lucia Specia, and Scott Wen-tau Yih (Eds.). Association for Computational Linguistics, 3045–3059
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Prefix-Tuning: Optimizing Continuous Prompts for Generation. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, ACL/IJCNLP 2021, (Volume 1: Long Papers), Virtual Event, August 1-6, 2021 , Chengqing Zong, Fei Xia, Wenjie Li, and Roberto Navigli (Eds.). Association for Computational Linguistics, 4582–4597
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Recommending API Function Calls and Code Snippets to Support Software Development
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Few-shot training llms for project-specific code-summarization. In Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering . 1–5
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Large language models are zero-shot reasoners
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Lewis Tunstall, Leandro Von Werra, and Thomas Wolf. 2022 · 2022
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Expectation vs. experience: Evaluating the usability of code generation tools powered by large language models. In Chi conference on human factors in computing systems extended abstracts . 1–7
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Accurate generation of trigger-action programs with domain-adapted sequence-to-sequence learning. In Proceedings of the 30th IEEE/ACM International Conference on Program Comprehension . 99–110
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Antonio Mastropaolo, Luca Pascarella, Emanuela Guglielmi, Matteo Ciniselli, Simone Scalabrino, Rocco Oliveto, and Gabriele Bavota. 2023 · 2023
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Artificial intelligence vs. software engineers: An empirical study on performance and efficiency using chatgpt. In Proceedings of the 33rd Annual International Conference on Computer Science and Software Engineering . 24–33
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Exploring the Effectiveness of Large Language Models in Generating Unit Tests
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Codegeex: A pre-trained model for code generation with multilingual evaluations on humaneval-x
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Exploring ChatGPT’s code refactoring capabilities: An empirical study
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Exploring the potential of chatgpt in automated code refinement: An empirical study. In Proceedings of the 46th IEEE/ACM International Conference on Software Engineering . 1–13
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Can ChatGPT Support Developers? An Empirical Evaluation of Large Language Models for Code Generation
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Is your code generated by chatgpt really correct? rigorous evaluation of large language models for code generation
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No need to lift a finger anymore? Assessing the quality of code generation by ChatGPT
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