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This content will become publicly available on October 1, 2023

Title: URegM: A Unified Prediction Model of Resource Consumption for Refactoring Software Smells in Open Source Cloud
The low cost and rapid provisioning capabilities have made the cloud a desirable platform to launch complex scientific applications. However, resource utilization optimization is a significant challenge for cloud service providers, since the earlier focus is provided on optimizing resources for the applications that run on the cloud, with a low emphasis being provided on optimizing resource utilization of the cloud computing internal processes. Code refactoring has been associated with improving the maintenance and understanding of software code. However, analyzing the impact of the refactoring source code of the cloud and studying its impact on cloud resource usage require further analysis. In this paper, we propose a framework called Unified Regression Modeling (URegM) which predicts the impact of code smell refactor- ing on cloud resource usage. We test our experiments in a real-life cloud environment using a complex scientific application as a workload. Results show that URegM is capable of accurately predicting resource consumption due to code smell refactoring. This will permit cloud service providers with advanced knowledge about the impact of refactoring code smells on resource consumption, thus allowing them to plan their resource provisioning and code refactoring more effectively.
Award ID(s):
1842054 1724898 2007829
Publication Date:
Journal Name:
Proceedings of ACM European Symposium on Software Engineering (ESSE 2022)
Sponsoring Org:
National Science Foundation
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