Towards Semantic Detection of Smells in Cloud Infrastructure Code

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.