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Cloud-based software-as-a-service (SaaS) have gained popularity due to theirlow cost and elasticity. However, like other software, SaaS applications sufferfrom code smells, which can drastically affect functionality and resourceusage. Code smell is any design in the source code that indicates a deeperproblem. The software community deploys automated refactoring to eliminatesmells which can improve performance and also decrease the usage of criticalresources. However, studies that analyze the impact of automatic refactoringsmells in SaaS on resources such as CPU and memory have been conducted to alimited extent. Here, we aim to fill that gap and study the impact on resourceusage of SaaS applications due to automatic refactoring of seven classic codesmells: god class, feature envy, type checking, cyclic dependency, shotgunsurgery, god method, and spaghetti code. We specified six real-life SaaSapplications from Github called Zimbra, OneDataShare, GraphHopper, Hadoop,JENA, and JAMES which ran on Openstack cloud. Results show that refactoringsmells by tools like JDeodrant and JSparrow have widely varying impacts on theCPU and memory consumption of the tested applications based on the type ofsmell refactored. We present the resource utilization impact of each smell andalso discuss the potential reasons leading to that effect.

The Impact of Auto-Refactoring Code Smells on the Resource Utilization of Cloud Software