An Exploratory Study on Architectural Smell Refactoring Using Large Languages Models

Architectural smells are abundant in codebases and regularly hinder the development of stable and maintainable code. Understanding and removing these elements can consume a huge amount of developers' time, who often need to prioritize implementing new features. This causes a substantial increase in Technical Debt, compromising the scalability and maintainability of the codebases, at time bringing the development to a standstill. Meanwhile, the use of Large Language Models for small error correction is constantly growing, bringing the attention of an ever-wider audience to these technologies. This study explores a first approach to use Large Language Models to suggest refactoring for architectural smells, with a focus on Cyclic Dependencies smells. We study the use of detailed prompt and Retrieval-Augmented Generation (RAG) to enhance LLMs, and we study local vs cloud LLMs. The results are promising, also validated with a series of interviews with students and developers, and highlight how additional and precise context is key to enhance the use of LLMs to propose refactoring suggestions. A multi-agent approach seems to be more suited when increasing the complexity of the smells.