CSEPrompts: A Benchmark of Introductory Computer Science Prompts

TL;DR

This paper introduces CSEPrompts, a comprehensive benchmark of programming exercises and questions from introductory CS courses, to evaluate LLMs' capabilities in generating code and answering related questions.

Contribution

It provides a new benchmark dataset for assessing LLM performance on introductory CS prompts and offers experimental analysis of various models' effectiveness.

Findings

LLMs show varying proficiency in generating Python code.

Performance differences observed among different LLMs.

CSEPrompts serves as a valuable tool for future research in AI-assisted CS education.

Abstract

Recent advances in AI, machine learning, and NLP have led to the development of a new generation of Large Language Models (LLMs) that are trained on massive amounts of data and often have trillions of parameters. Commercial applications (e.g., ChatGPT) have made this technology available to the general public, thus making it possible to use LLMs to produce high-quality texts for academic and professional purposes. Schools and universities are aware of the increasing use of AI-generated content by students and they have been researching the impact of this new technology and its potential misuse. Educational programs in Computer Science (CS) and related fields are particularly affected because LLMs are also capable of generating programming code in various programming languages. To help understand the potential impact of publicly available LLMs in CS education, we introduce CSEPrompts, a framework with hundreds of programming exercise prompts and multiple-choice questions retrieved from introductory CS and programming courses. We also provide experimental results on CSEPrompts to evaluate the performance of several LLMs with respect to generating Python code and answering basic computer science and programming questions.