A Survey on Large Language Models for Code Generation
Hong Kong University of Science and Technology · Naver (South Korea)
Abstract
Large Language Models (LLMs) have garnered remarkable advancements across diverse code-related tasks, known as Code LLMs, particularly in code generation that generates source code with LLM from natural language descriptions. This burgeoning field has captured significant interest from both academic researchers and industry professionals due to its practical significance in software development, e.g., GitHub Copilot . Despite the active exploration of LLMs for a variety of code tasks, either from the perspective of Natural Language Processing (NLP) or Software Engineering (SE) or both, there is a noticeable absence of a comprehensive and up-to-date literature review dedicated to LLM for code generation. In…
Citation impact
- FWCI
- 33.80
- Percentile
- 100%
- References
- 89
Authors
5- JJJuyong JiangCorresponding
- FWFan Wang
- JSJiasi Shen
Hong Kong University of Science and Technology
- SKSungju Kim
Naver (South Korea)
- SKSunghun Kim
Naver (South Korea)
Topics & keywords
- Code review
- Code (set theory)
- Natural language generation
- Natural language
- Code generation
- Software
- Variety (cybernetics)
- Field (mathematics)