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A Comprehensive Survey of AI-Driven Advancements and Techniques in Automated Program Repair and Code Generation
This survey paper examines the recent advancements in automated program repair (APR) and code generation using Large Language Models (LLMs). The paper reviews 27 recent research papers, categorizing them into two groups: APR with LLM integration and code generation using LLMs. The authors identify trends in these fields, such as the use of LLMs, feedback loops for iterative code improvement, and open-source models. The paper also discusses the challenges of ensuring functional correctness and security in AI-driven software development and outlines future research directions.
https://arxiv.org/pdf/2411.07586
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By AIPPDThis survey paper examines the recent advancements in automated program repair (APR) and code generation using Large Language Models (LLMs). The paper reviews 27 recent research papers, categorizing them into two groups: APR with LLM integration and code generation using LLMs. The authors identify trends in these fields, such as the use of LLMs, feedback loops for iterative code improvement, and open-source models. The paper also discusses the challenges of ensuring functional correctness and security in AI-driven software development and outlines future research directions.
https://arxiv.org/pdf/2411.07586