Towards Semantic Detection of Smells in Cloud Infrastructure Code
This addresses the issue of maintaining code quality in cloud deployment descriptions for developers, but it appears incremental as it builds on existing knowledge graph and rule-based methods.
The paper tackles the problem of detecting software smells in cloud infrastructure code by presenting a knowledge-driven approach using SPARQL-based rules over OWL 2 knowledge graphs, demonstrating feasibility through a prototype and three case studies.
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.