A Review of Research on AI-Assisted Code Generation and AI-Driven Code Review

Abstract

With the significant breakthroughs of deep learning technologies such as large language models (LLMs) in the field of code analysis, AI has evolved from an auxiliary tool to a key technology that deeply participates in code optimization and resolving performance issues. As modern software system architectures become increasingly complex, the requirements for their performance have also become more stringent. During the coding stage, developers find it difficult to effectively identify and resolve potential performance issues using traditional methods. This review focuses on the application of artificial intelligence in two key areas: AI-assisted intelligent code generation and AI-povered code review. The review systematically analyzed the application of LLMs in software development, revealing a situation where efficiency gains coexist with quality challenges. In terms of code generation, models such as Code Llama and Copilot have significantly accelerated the development process. In the field of code review, AI can effectively handle code standards and low-severity defects. However, in the future, this field still needs to address the issues of the reliability and security of the code generated by LLMs, as well as the insufficient explainability of the results of automated performance analysis. The future research focus in this field lies in addressing issues such as the lack of interpretability and insufficient domain knowledge of LLMs. It is necessary to prioritize enhancing the reliability of AI recommendations and promoting the transformation of AI from an auxiliary tool to an intelligent Agent with self-repair capabilities, in order to achieve a truly efficient and secure human-machine collaboration paradigm. This article systematically reviews the relevant progress, aiming to promote the transformation of software engineering from an artificial-driven model to an AI-enhanced automated paradigm. It provides theoretical references for ensuring the quality of backend code, improving product delivery speed, and enhancing system reliability.