Recommender Systems for Unified Modeling Language and Vice Versa—A Systematic Literature Review
Recommender systems (RSs) are fundamental tools that address data redundancy and serve as intelligent supplements for tasks such as data retrieval and refinement by analyzing user behavior. Nowadays, RSs are utilized in various domains, ranging from filtering web news based on user preferences to re...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10855999/ |
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| Summary: | Recommender systems (RSs) are fundamental tools that address data redundancy and serve as intelligent supplements for tasks such as data retrieval and refinement by analyzing user behavior. Nowadays, RSs are utilized in various domains, ranging from filtering web news based on user preferences to recommending movies, music, books, and articles in e-commerce. Additionally, these systems are extensively employed to facilitate software engineering activities, including modeling. Modeling environments are enriched with RSs that assist in building models by providing recommendations based on previous solutions to similar problems within the same domain. Consequently, there is growing research interest in approaches that aid the modeling process. This paper presents a systematic literature review (SLR) that analyzes how recommender systems techniques are used to suggest UML diagrams, as well as the role of UML diagrams in describing recommender systems. In addition, it discusses methods for evaluating primary studies, the challenges that primary studies have addressed, and the domains of study that primary studies have targeted (based on an analysis of 4789 papers). We believe this study will guide researchers and professionals in identifying recommender system techniques for generating UML diagram suggestions and understanding the overall purpose of using UML diagrams. Furthermore, it may contribute to a broader understanding of the research process and inspire future research on recommender system techniques within other modeling languages. The results show that 45% of the studies use content-based techniques to suggest UML diagrams, with 77% of the recommendations being structural diagrams (such as class diagrams). On the other hand, to design the components of the proposed approaches (recommender systems), behavioral diagrams are generally used (53% on average), focusing on knowledge-based techniques (28% on average). Finally, the study shows that researchers use content-based (38%) and knowledge-based (41%) techniques to recommend design models. The analysis revealed that the following challenges were identified: 19 studies dealt with the cold start problem, 20 studies with sparsity issues, 11 studies with scalability concerns, 3 studies with diversity challenges, and 12 studies with other types of challenges. |
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| ISSN: | 2169-3536 |