Conversing with Copilot: Exploring Prompt Engineering for Solving CS1 Problems Using Natural Language (2210.15157v1)

Abstract: 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.

Analyzing the Efficacy of Prompt Engineering with GitHub Copilot for CS1 Problems

The paper "Conversing with Copilot: Exploring Prompt Engineering for Solving CS1 Problems Using Natural Language" provides a focused examination of GitHub Copilot's effectiveness in handling introductory programming issues using natural language prompts. The authors investigate specific dynamics at play when leveraging Copilot, an AI-based code generation tool, to solve a defined set of programming problems, while rigorously analyzing prompt engineering methods to enhance its performance.

Background and Motivation

GitHub Copilot, powered by the Codex model, represents a significant advancement in AI-driven code generation. Developers and educators are increasingly exploring how such technologies could reshape coding practices, particularly in educational spheres. While prior investigations have highlighted Copilot's initial competency in resolving basic CS1 problems, this research aims to uncover insights into its performance limitations, evaluating contexts where conventional approaches might initiate failures.

Methodology

The research is structured around 166 Python exercises sourced from an established repository, CodeCheck. These exercises span a range of complexity within four primary categories: Branches, Strings, Lists (Simple Exercises), and Two-Dimensional Arrays. The procedural approach involves providing natural language problem descriptions to Copilot and recording its initial solution attempt. When initial solutions fail, prompts are modified to assist Copilot in generating correct outputs.

Findings on Initial Performance

Initial evaluations reveal that Copilot successfully tackles nearly half of the problems on its first attempt. This reinforces the capacity of Copilot to engage with straightforward coding tasks effectively. However, success rates vary significantly across different problem categories. Some categories, particularly those involving list manipulations, exhibit more instances of failure, indicating areas where Copilot struggles with procedural complexities.

Effectiveness of Prompt Engineering

Prompt engineering emerges as a powerful tool in this framework, with around 61% of initially unsolved problems reaching successful outcomes following prompt adjustments. This process typically involves refining problem descriptions or breaking down tasks into clearer, more explicit computational instructions resembling pseudocode.

The paper presents significant evidence that prompt engineering transforms interactions with Copilot, thereby promoting deeper computational thinking. For several categories, especially those involving intricate logic or multi-faceted looping structures, modified prompts result in better alignment of Codex-generated solutions with expected outcomes.

Challenges and Common Failure Modes

Despite prompt engineering efforts, a subset of problems remains unsolved. These failures are generally categorized as conceptual ambiguities, verbose prompts, poor initial prompting strategies, or inherent limitations in Copilot’s comprehension capabilities. Specific problem types, such as those involving neighbor swaps or degenerate arrays, consistently challenge the model, suggesting inherent deficiencies in addressing complex logic through non-explicit prompts.

Implications and Future Directions

The research underscores a critical pedagogical implication: students must develop skills in crafting effective problem statements to interact successfully with AI models. Furthermore, prompt engineering may soon represent a necessary facet of computational pedagogy, given its evident role in influencing AI outputs. As AI-driven programming agents evolve, educational frameworks may increasingly need to incorporate strategies that teach and assess student competency in these AI interactions.

This work opens pathways for further inquiry into the real-world educational impacts of AI models like Copilot. As these models continuously enhance, understanding the depth and scope of prompt engineering will likely inform not only teaching strategies but also ethical standards within educational environments. The paper acts as a cornerstone for future explorations into how foundational models can adaptively support and revolutionize programming education.