论文标题
在云基础架构代码中探索气味的语义检测
Towards Semantic Detection of Smells in Cloud Infrastructure Code
论文作者
论文摘要
云应用程序的自动部署和管理依赖于其部署拓扑的描述，通常称为基础架构代码。随着应用程序的复杂性及其部署模型的增加，开发人员无意间将软件气味引入了此类代码规范，例如违反良好的编码实践，模块化结构等。本文提出了一种知识驱动的方法，使开发人员能够在部署描述中识别上述气味。我们在基于模式的猫头鹰2知识图中捕获部署模型的基于SPARQL的规则检测到气味。我们通过原型和三个案例研究表明了方法的可行性。
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.
