Semantic-based architecture smell analysis
- Authors
- Chondamrongkul, Nacha; Sun, Jing; Warren, Ian; Lee, Scott Uk Jin
- Issue Date
- Oct-2020
- Publisher
- Association for Computing Machinery
- Keywords
- Architecture Smells; Model Checking; Ontology Web Language; Smell Detection; Software Architecture
- Citation
- Proceedings - 2020 IEEE/ACM 8th International Conference on Formal Methods in Software Engineering, FormaliSE 2020, pp 109 - 118
- Pages
- 10
- Indexed
- SCOPUS
- Journal Title
- Proceedings - 2020 IEEE/ACM 8th International Conference on Formal Methods in Software Engineering, FormaliSE 2020
- Start Page
- 109
- End Page
- 118
- URI
- https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/1831
- DOI
- 10.1145/3372020.3391564
- ISSN
- 0000-0000
2575-5099
- Abstract
- Software smells have negative impacts on the reliability and modifiability of software systems. The smells in architecture design can be cascaded down to the implementation level and cause issues that require much effort to fix. Therefore, early detection of the architecture smells can benefit the overall quality of the software system. This paper presents an integration of methods that formally define the software architecture design towards architecture smell detection. Our approach serves as a framework that allows the architectural structures and behaviours to be formally analysed based on a coherent technique. We evaluated the accuracy and performance of our approach with the models generated from open source projects. The results show that our approach is effective and functions well. © 2020 Copyright held by the owner/author(s). Publication rights licensed to ACM.
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