Comparing Large Language Models and Human Programmers for Generating Programming Code

Wenpin Hou

doi:10.1002/advs.202412279

Comparing Large Language Models and Human Programmers for Generating Programming Code

2024-12-30

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DOI：10.1002/advs.202412279

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The performance of seven large language models (LLMs) in generating programming code using various prompt strategies, programming languages, and task difficulties is systematically evaluated. GPT-4 substantially outperforms other LLMs, including Gemini Ultra and Claude 2. The coding performance of GPT-4 varies considerably with different prompt strategies. In most LeetCode and GeeksforGeeks coding contests evaluated in this study, GPT-4, employing the optimal prompt strategy, outperforms 85 percent of human participants in a competitive environment, many of whom are students and professionals with moderate programming experience. GPT-4 demonstrates strong capabilities in translating code between different programming languages and in learning from past errors. The computational efficiency of the code generated by GPT-4 is comparable to that of human programmers. GPT-4 is also capable of handling broader programming tasks, including front-end design and database operations. These results suggest that GPT-4 has the potential to serve as a reliable assistant in programming code generation and software development. A programming assistant is designed based on an optimal prompt strategy to facilitate the practical use of LLMs for programming.

Keywords:

artificial intelligence

computer programming

human-computer interaction

large language models

Comparing Large Language Models and Human Programmers for Generating Programming Code

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