An Empirical Evaluation on the Effect of Refactoring Code Smells Mobile Applications Android with ASATs on Resource Usage

Abstract

The Application is closely connected to mobile devices designed for many people and has maintenance. However, even maintenance can contain violations, such as code smells that affect non-functional requirements, specifically CPU and memory resources. When the software has a rapid use of resources, it gives rise to the phenomenon that the user may switch or uninstall the software. The solution to this phenomenon is to explore resource-related code smells and fix them by refactoring them. Developments to explore code smells came with ASATs, namely SonarQube, which 85,000 organizations are already using to speed up analyzing code in software. This topic is related to code smells, and the research objective is to investigate and compare the performance of the original versions and single or cumulative refactored versions of Android mobile software using the Design Research Methodology (DRM) approach. Code smells are represented based on the classification on SonarQube, namely Blocker, Critical, Major, and Minor, with code smells such as HashMap Usage, Member Ignoring Method, and Slow Loop. Aspects tested include Fixed Detection Ratio (FDR), improvement, CPU, and memory usage. Based on the results of the research that has been done, it shows that there is depreciation of code smells, which is proven to significantly increase CPU performance in a single refactoring, namely Member Ignoring Method and Critical by 7.7% and 9.90%, respectively. Additionally, memory performance improvements had a more significant effect across both single and cumulative refactoring.