Towards semantic detection of smells in cloud infrastructure code

I. Kumara; Z. Vasileiou; G. Meditskos; D.A. Tamburri; W.-J. Van Den Heuvel; A. Karakostas; S. Vrochidis; I. Kompatsiaris

doi:10.1145/3405962.3405979

Towards semantic detection of smells in cloud infrastructure code

I. Kumara, Z. Vasileiou, G. Meditskos, D.A. Tamburri, W.-J. Van Den Heuvel, A. Karakostas, S. Vrochidis, I. Kompatsiaris

Information Systems & Oper. Mgt.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review

19 Citations (Scopus)

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.

Original language	English
Title of host publication	WIMS 2020: Proceedings of the 10th International Conference on Web Intelligence, Mining and Semantics
Place of Publication	New York
Publisher	ACM
Pages	63-67
DOIs	https://doi.org/10.1145/3405962.3405979
Publication status	Published - Jun 2020

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title = "Towards semantic detection of smells in cloud infrastructure code",

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.",

author = "I. Kumara and Z. Vasileiou and G. Meditskos and D.A. Tamburri and {Van Den Heuvel}, W.-J. and A. Karakostas and S. Vrochidis and I. Kompatsiaris",

year = "2020",

month = jun,

doi = "10.1145/3405962.3405979",

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Towards semantic detection of smells in cloud infrastructure code. / Kumara, I.; Vasileiou, Z.; Meditskos, G. et al.
WIMS 2020: Proceedings of the 10th International Conference on Web Intelligence, Mining and Semantics. New York: ACM, 2020. p. 63-67.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review

TY - GEN

T1 - Towards semantic detection of smells in cloud infrastructure code

AU - Kumara, I.

AU - Vasileiou, Z.

AU - Meditskos, G.

AU - Tamburri, D.A.

AU - Van Den Heuvel, W.-J.

AU - Karakostas, A.

AU - Vrochidis, S.

AU - Kompatsiaris, I.

PY - 2020/6

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N2 - 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.

AB - 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.

U2 - 10.1145/3405962.3405979

DO - 10.1145/3405962.3405979

M3 - Conference contribution

SP - 63

EP - 67

BT - WIMS 2020: Proceedings of the 10th International Conference on Web Intelligence, Mining and Semantics

PB - ACM

CY - New York

ER -

Towards semantic detection of smells in cloud infrastructure code

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