Description

Project Background

Recommender systems are valuable tools across various domains, offering users personalized suggestions based on preferences, needs, or constraints. In the field of software architecture, particularly within security-centric contexts, such systems can assist architects in making informed design decisions. As part of the Security-Centric Architecture Modeling (SCAM) Approach, which is being researched at the Software Construction research group, there is a need for a recommender system that can provide architects with design recommendations tailored to specific security requirements. By leveraging different types of recommender systems, including content-based and constraint-based systems, this project seeks to enhance the decision-making process in security-driven software architecture.

Project Description

This master thesis aims to conduct an extensive literature review to explore existing approaches to recommender systems, such as constraint-based and content-based recommender systems, and analyze their applications in various domains. The primary objective is to provide an overview of existing recommender approaches in order to compare them with or adapt them for use in security-centric software architecture. For this purpose, both academic and grey literature should be analysed. Building on this, the student will develop a taxonomy that classifies these recommender system approaches according to their characteristics, use cases, and actual or potential applicability for practical use cases.