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The aim is to provide a comprehensive literature review on large language models used for code generation and their advancements.

A Survey on Large Language Models for Code Generation

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The aim is to provide a comprehensive literature review on large language models used for code generation and their advancements.
Conducted a systematic literature review on LLMs for code generation.
Introduced a taxonomy for categorizing recent developments.
Presented a comparative analysis of performance using benchmarks like HumanEval, MBPP, and BigCodeBench.
Identified challenges and opportunities in the transition from academia to practical applications.
Highlighted the rapid progress in large language model capabilities for generating code.
Showed differences in performance across various benchmarks based on task difficulty.
Outlined ethical implications and environmental impacts related to LLMs in code generation.

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This survey reviews advancements in code generation with large language models, suggesting vital insights for researchers and practitioners.

Jiang et al. (2025) studied this question.

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This survey reviews advancements in code generation with large language models, suggesting vital insights for researchers and practitioners.