A Systematic Survey on Large Language Models for Code Generation
DOI:
https://doi.org/10.14500/aro.12159Keywords:
Benchmarking, Code Generation, Evaluation Metrics, Large Language ModelsAbstract
The rapid development of large language models (LLMs) has transformed code generation, offering powerful tools for automating software development tasks. However, evaluating generated code’s quality, security, and effectiveness remains a significant challenge. The present systematic survey comprehensively analyses studies published between 2021 and 2024, focusing on utilizing LLMs in the code generation process. The survey explored ten research questions, such as the most commonly used programming languages, the metrics employed to evaluate the quality of code, and scenarios in which LLMs are applied by developers during the software development process, outlining the scope in which prompt engineering influences code generation and security concerns with the types of benchmarks, models evaluated, and code analysis tools used in studies. The findings indicate that the most frequently used evaluation metrics in code generation are Pass@k and Bilingual Evaluation Understudy. It also shows that Python, Java, and C++ are the most widely used languages. Furthermore, identifying security vulnerabilities and establishing robust evaluation metrics remain challenges. This survey underlines present practices, detects gaps, and suggests future research to enhance the reliability and security of code generated by LLMs in real-world applications.
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