Usage of Large Language Model for Code Generation Tasks: A Review
Problem
The literature lacked an exhaustive analysis of the benefits and drawbacks of using large language models for generating simple and complex code.
Why it matters
Understanding LLM strengths and weaknesses in code generation affects software development practice, debugging, and ethical safeguards.
What the AI did
No AI system solved a problem; the authors performed a PRISMA-based literature review of 66 papers (2021-2023) on LLM code generation, answering 4 research questions.
Results
Reviewed 66 papers; found Python dominance as preferred language and a significant research gap on ethical constraints.
Impact
Maps current capabilities and gaps of LLM code generation for future research.
Record
| Venue | SN Computer Science |
|---|---|
| Published | 2025 |
| Work type | article |
| Authors | Stefano Bistarelli, Marco Fiore, Ivan Mercanti, Marina Mongiello |
| Citations | 15 (OpenAlex, at import time) |
| References | 64 |
| Model or method | GPT-4, CodeLlama (reviewed models) |
| Organization | not stated in source |
| Primary source | https://doi.org/10.1007/s42979-025-04241-5 |
| Open access | hybrid |
| Keywords | large language models, code generation, PRISMA, literature review, GPT-4, CodeLlama |
Importance rating
real but narrower advance
LLM code generation affects software development practice, but this is a PRISMA literature synthesis of 66 papers mapping capabilities and gaps, not a new capability itself.
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