Towards semantic detection of smells in cloud infrastructure code
Kumara,I. Vasileiou,Z. Meditskos,G. Tamburri,D.A. Van Den Heuvel,W.-J. Karakostas,A. Vrochidis,S. Kompatsiaris,I.
Kumara,I.
Vasileiou,Z.
Meditskos,G.
Tamburri,D.A.
Van Den Heuvel,W.-J.
Karakostas,A.
Vrochidis,S.
Kompatsiaris,I.
Abstract
Automated deployment and management of Cloud applications relies on descriptions of their deployment topologies, often referred to as Infrastructure Code. As the complexity of applications and their deployment models increases, developers inadvertently introduce software smells to such code specifications, for instance, violations of good coding practices, modular structure, and more. This paper presents a knowledge-driven approach enabling developers to identify the aforementioned smells in deployment descriptions. We detect smells with SPARQL-based rules over pattern-based OWL 2 knowledge graphs capturing deployment models. We show the feasibility of our approach with a prototype and three case studies.
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Date
2020-06
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ACM
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Kumara, I, Vasileiou, Z, Meditskos, G, Tamburri, D A, Van Den Heuvel, W-J, Karakostas, A, Vrochidis, S & Kompatsiaris, I 2020, Towards semantic detection of smells in cloud infrastructure code. in WIMS 2020: Proceedings of the 10th International Conference on Web Intelligence, Mining and Semantics. ACM, New York, pp. 63-67. https://doi.org/10.1145/3405962.3405979
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info:eu-repo/semantics/openAccess