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A code generation model generates code by taking a prompt from a code comment, existing code, or a combination of both.
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GitHub Copilot AI pair programmer: Asset or Liability?
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CodeFill: Multi-token Code Completion by Jointly Learning from Structure and Naming Sequences. In 44th Int’l Conference on Software Engineering (ICSE)
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A Conversational Paradigm for Program Synthesis
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Chat completions
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JaCoCo - Java Code Coverage Library
2023 · 2023
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MultiPL-E: a scalable and polyglot approach to benchmarking neural code generation
Federico Cassano, John Gouwar, Daniel Nguyen, Sydney Nguyen, Luna Phipps-Costin, Donald Pinckney, Ming-Ho Yee, Yangtian Zi, Carolyn Jane Anderson, Molly Q Feldman, et al · 2023
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CODAMOSA: Escaping Coverage Plateaus in Test Generation with Pre-trained Large Language Models. In 45th Int’l Conf. on Software Engineering, ser. ICSE
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StarCoder: may the source be with you!
R. Li, L. Ben allal, Y. Zi, N. Muennighoff, D. Kocetkov, …, and H. de Vries. 2023a · 2023
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Nuances are the Key: Unlocking ChatGPT to Find Failure-Inducing Tests with Differential Prompting. In 2023 38th IEEE/ACM Int’l Conf. on Automated Software Engineering (ASE) . IEEE, 14–26
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Software Testing and Code Refactoring: A Survey with Practitioners. In 2023 IEEE Int’l Conf. on Software Maintenance and Evolution (ICSME) . 500–507
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Can Generative Pre-Trained Transformers (GPT) Pass Assessments in Higher Education Programming Courses?. In Proc’d. of the 2023 Conf. on Innovation and Technology in Computer Science Education V. 1 (Turku, Finland) (ITiCSE 2023) . ACM, New York, NY, USA, 117–123
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Adaptive Test Generation Using a Large Language Model
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Survey reveals AI’s impact on the developer experience | | The GitHub Blog
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Zero-shot Prompting for Code Complexity Prediction Using GitHub Copilot. In 2023 The 2nd Intl. Workshop on NL-based Software Engineering
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