Assessing the Promise and Pitfalls of ChatGPT for Automated Code
Generation
- URL: http://arxiv.org/abs/2311.02640v1
- Date: Sun, 5 Nov 2023 12:56:40 GMT
- Title: Assessing the Promise and Pitfalls of ChatGPT for Automated Code
Generation
- Authors: Muhammad Fawad Akbar Khan, Max Ramsdell, Erik Falor, Hamid Karimi
- Abstract summary: This paper presents a comprehensive evaluation of the code generation capabilities of ChatGPT, a prominent large language model.
A dataset of 131 code-generation prompts across 5 categories was curated to enable robust analysis.
Code solutions were generated by both ChatGPT and humans for all prompts, resulting in 262 code samples.
- Score: 2.0400340435492272
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: This paper presents a comprehensive evaluation of the code generation
capabilities of ChatGPT, a prominent large language model, compared to human
programmers. A novel dataset of 131 code-generation prompts across 5 categories
was curated to enable robust analysis. Code solutions were generated by both
ChatGPT and humans for all prompts, resulting in 262 code samples. A meticulous
manual assessment methodology prioritized evaluating correctness,
comprehensibility, and security using 14 established code quality metrics. The
key findings reveal ChatGPT's strengths in crafting concise, efficient code
with advanced constructs, showcasing strengths in data analysis tasks (93.1%
accuracy) but limitations in visual-graphical challenges. Comparative analysis
with human code highlights ChatGPT's inclination towards modular design and
superior error handling. Additionally, machine learning models effectively
distinguished ChatGPT from human code with up to 88% accuracy, suggesting
detectable coding style disparities. By providing profound insights into
ChatGPT's code generation capabilities and limitations through quantitative
metrics and qualitative analysis, this study makes valuable contributions
toward advancing AI-based programming assistants. The curated dataset and
methodology offer a robust foundation for future research in this nascent
domain. All data and codes are available on
https://github.com/DSAatUSU/ChatGPT-promises-and-pitfalls.
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