Towards Semantic Detection of Smells in Cloud Infrastructure Code
Indika Kumara, Zoe Vasileiou, Georgios Meditskos, Damian A., Tamburri, Willem-Jan Van Den Heuvel, Anastasios Karakostas, Stefanos, Vrochidis, Ioannis Kompatsiaris

TL;DR
This paper introduces a knowledge-driven method using OWL 2 and SPARQL to detect software smells in cloud infrastructure code, aiding developers in maintaining better deployment practices.
Contribution
It presents a novel approach leveraging semantic web technologies for automated detection of smells in Infrastructure as Code, demonstrated through a prototype and case studies.
Findings
Feasibility of using OWL 2 knowledge graphs for smell detection
Effective identification of code smells in deployment models
Prototype demonstrates practical applicability
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.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
