MA-RAG: Automating Role Engineering for RESTful APIs with Multi-Head Attention and Retrieval-Augmented Generation

Abstract

This paper addresses the role engineering problem for RESTful applications and proposes a role engineering method based on multi-head attention and Retrieval Augmented Generation called MA-RAG. The method first performs fine-grained control flow analysis on the system source code to extract permission information of API handlers. Then, using basic blocks as units, it employs pre-trained code models to convert the source code into semantic vectors, which are stored in the retrieval augmented generation model. On this basis, a call chain structure tree is constructed with permissions as the center, utilizing the multi-head attention mechanism to aggregate semantic information of different code granularities from bottom to top, with each attention head corresponding to a role engineering objective. Finally, the root vectors of each permission tree are subjected to self-supervised clustering to adaptively determine the number of roles and perform division. We evaluated MA-RAG on 284 real-world software systems, and the results show that compared with other methods, MA-RAG can significantly save time overhead, reduce the number of generated roles, lower the role permission overlap rate, and improve the interpretability score.