A survey on Large Language Models for code generation [pdf] (dl.acm.org)

A recent survey on Large Language Models (LLMs) for code generation has been released, shedding light on the current state and future potential of these AI-driven tools within the software development community. The survey offers an in-depth analysis of various LLMs, assessing their strengths and weaknesses in generating code, including their effectiveness in tasks such as automatic completion, debugging, and documentation generation. This resource serves as a critical reference for researchers and developers looking to leverage LLMs, providing insights into model architectures, training methodologies, and application contexts. The significance of this survey lies in its potential to guide advancements in AI/ML as it highlights the growing integration of LLMs in software engineering practices. As coding becomes increasingly automated, understanding the performance and limitations of these models is essential for improving software quality and efficiency. Key implications include a deeper exploration of security and ethical considerations associated with code generation, as well as recommendations for future research directions aimed at enhancing the reliability and safety of AI-generated code. This survey adds valuable knowledge to the evolving landscape of AI in programming and stands to influence both academic research and practical deployments in the industry.