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Comparing large language models and human programmers for generating programming code

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arxiv 2403.00894 v2 pith:SRJWDHGQ submitted 2024-03-01 cs.SE cs.AIcs.CLcs.PL

classification cs.SEcs.AIcs.CLcs.PL
keywords gpt-4codeprogramminghumanlanguagelargemodelsprompt
verification ladder T0 review T1 audit T2 compute T3 formal
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We systematically evaluated the performance of seven large language models in generating programming code using various prompt strategies, programming languages, and task difficulties. GPT-4 substantially outperforms other large language models, 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. Additionally, 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. These results suggest that GPT-4 has the potential to serve as a reliable assistant in programming code generation and software development.

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Cited by 3 Pith papers

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    Automated refactoring of deprecated Java APIs succeeds 71 to 82 percent of the time when Javadoc contains code hints, and at most 14 percent without them.

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  3. SmartLLMSentry: A Comprehensive LLM Based Smart Contract Vulnerability Detection Framework

    cs.CR 2024-11 conditional novelty 4.0 of 10

    A fine-tuned GPT-3.5 model generated static analysis detector conditions for five smart contract vulnerability classes with 92.1% exact match on a 38-sample test set.

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