@techreport{oai:ipsj.ixsq.nii.ac.jp:02007720,
 author = {Qiao,Lin and Profir-petru,Pârţachi and Takashi,Kobayashi and Qiao Lin and Profir-petru Pârţachi and Takashi Kobayashi},
 issue = {20},
 month = {Mar},
 note = {Microservice documentation is critical yet hard to maintain due to its complex and distributed architecture. Developers use Large Language Models (LLMs) with Retrieval Augmented Generation (RAG) to automate the creation and update of microservice documentations; however, this approach has limitations. Standard RAG is blind to the high level, global structural dependencies among services and relies solely on semantic similarity (Structural Blindness). As a result, LLMs suffer from missing key dependencies and information during generation and leading to inaccurate documentation. We introduce a Graph RAG framework that integrates structural awareness into the generation process. A Knowledge Graph (KG) and a GNN are used to help retrieve the most relevant dependencies for the LLM during generation. This ensures the generated documentation is both semantically coherent and architecturally complete, addressing some of the current limitation. As the result, we improve on three metrics over our baselines: Correctness (0.66-1.64), Completeness (0.86-2.24), and Faithfulness (0.64-1.62)., Microservice documentation is critical yet hard to maintain due to its complex and distributed architecture. Developers use Large Language Models (LLMs) with Retrieval Augmented Generation (RAG) to automate the creation and update of microservice documentations; however, this approach has limitations. Standard RAG is blind to the high level, global structural dependencies among services and relies solely on semantic similarity (Structural Blindness). As a result, LLMs suffer from missing key dependencies and information during generation and leading to inaccurate documentation. We introduce a Graph RAG framework that integrates structural awareness into the generation process. A Knowledge Graph (KG) and a GNN are used to help retrieve the most relevant dependencies for the LLM during generation. This ensures the generated documentation is both semantically coherent and architecturally complete, addressing some of the current limitation. As the result, we improve on three metrics over our baselines: Correctness (0.66-1.64), Completeness (0.86-2.24), and Faithfulness (0.64-1.62).},
 title = {Structure-Aware Enhanced LLMs via Knowledge Graphs for Microservice Architecture Documentation},
 year = {2026}
}