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Conversing with Copilot: Exploring Prompt Engineering for Solving CS1 Problems Using Natural Language

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arxiv 2210.15157 v1 pith:BUJYE5WQ submitted 2022-10-27 cs.HC cs.AI

classification cs.HCcs.AI
keywords copilotproblemslanguagenaturalcodeavailabledevelopmentengineering
verification ladder T0 review T1 audit T2 compute T3 formal
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GitHub Copilot is an artificial intelligence model for automatically generating source code from natural language problem descriptions. Since June 2022, Copilot has officially been available for free to all students as a plug-in to development environments like Visual Studio Code. Prior work exploring OpenAI Codex, the underlying model that powers Copilot, has shown it performs well on typical CS1 problems thus raising concerns about the impact it will have on how introductory programming courses are taught. However, little is known about the types of problems for which Copilot does not perform well, or about the natural language interactions that a student might have with Copilot when resolving errors. We explore these questions by evaluating the performance of Copilot on a publicly available dataset of 166 programming problems. We find that it successfully solves around half of these problems on its very first attempt, and that it solves 60\% of the remaining problems using only natural language changes to the problem description. We argue that this type of prompt engineering, which we believe will become a standard interaction between human and Copilot when it initially fails, is a potentially useful learning activity that promotes computational thinking skills, and is likely to change the nature of code writing skill development.

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  1. CodeSCM: Causal Analysis for Multi-Modal Code Generation

    cs.CL 2025-02 conditional novelty 5.0 of 10

    A causal framework with latent mediators quantifies how prompt modalities affect code LLMs, finding that input-output examples and function-header names are influential beyond natural language instructions.

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