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Recent developments in deep learning have resulted in code-generation models that produce source code from natural language and code-based prompts with high accuracy.
Language Models are Few-shot Learners
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Plagiarism in the Age of Massive Generative Pre-trained Transformers (GPT-3)
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Comparative Study Between Automatic Hint Generation Approaches in Intelligent Programming Tutors
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Introducing Amazon CodeWhisperer, the ML-powered coding companion
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James Prather, Raymond Pettit, Kayla McMurry, Alani Peters, John Homer, and Maxine Cohen. 2018 · 2018
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Plagiarism in Programming Assessments: A Systematic Review
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Compiler Error Messages Considered Unhelpful: The Landscape of Text-Based Programming Error Message Research. In Proceedings of the Working Group Reports on Innovation and Technology in Computer Science Education (Aberdeen, Scotland) (ITiCSE-WGR ’19) . Association for Computing Machinery, New York, NY, USA, 177–210
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50 Years of CS1 at SIGCSE: A Review of the Evolution of Introductory Programming Education Research. In Proceedings of the 50th ACM Technical Symposium on Computer Science Education (Minneapolis, MN, USA) (SIGCSE ’19) . Association for Computing Machinery, New York, NY, USA, 338–344
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Can Mobile Gaming Psychology Be Used to Improve Time Management on Programming Assignments?. In Proceedings of the ACM Conference on Global Computing Education (Chengdu,Sichuan, China) (CompEd ’19) . Association for Computing Machinery, New York, NY, USA, 208–214
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Exploring the Applicability of Simple Syntax Writing Practice for Learning Programming. In Proceedings of the 50th ACM Technical Symposium on Computer Science Education (Minneapolis, MN, USA) (SIGCSE ’19) . Association for Computing Machinery, New York, NY, USA, 84–90
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Grounded Copilot: How Programmers Interact with Code-Generating Models
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Fooling MOSS Detection with Pretrained Language Models. In Proceedings of the 31st ACM International Conference on Information and Knowledge Management (Atlanta, GA, USA) (CIKM ’22) . Association for Computing Machinery, New York, NY, USA, 2933–2943
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CodeT: Code Generation with Generated Tests
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A Neural Network Solves, Explains, and Generates University Math Problems by Program Synthesis and Few-Shot Learning at Human Level
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The Robots Are Coming: Exploring the Implications of OpenAI Codex on Introductory Programming. In Australasian Computing Education Conference (Virtual Event, Australia) (ACE ’22) . Association for Computing Machinery, New York, NY, USA, 10–19
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Metacognition and Self-Regulation in Programming Education: Theories and Exemplars of Use
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Asleep at the Keyboard? Assessing the Security of GitHub Copilot’s Code Contributions. In 2022 IEEE Symposium on Security and Privacy (SP) . 754–768
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Pop Quiz! Can a Large Language Model Help With Reverse Engineering?
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Getting By With Help From My Friends: Group Study in Introductory Programming Understood as Socially Shared Regulation. In Proceedings of the 2022 ACM Conference on International Computing Education Research - Volume 1 (Lugano and Virtual Event, Switzerland) (ICER ’22) . Association for Computing Machinery, New York, NY, USA, 164–176
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