EPE‐Mobile—A framework for early performance estimation of mobile applications. (24th August 2017)

• Record Type: Journal Article
• Authors: Fernandes, Thiago Soares; Moreira, Álvaro Freitas; Cota, Érika
• Is Part Of: Software, practice & experience. Volume 48:Number 1(2018)
• Page Start: 85
• Page End: 104
• Publication Date: 2017-08-24
• Subjects: Android -- aspect‐oriented programming -- benchmarks -- embedded systems -- mobile systems -- nonfunctional requirements -- performance evaluation; Computer software -- Periodicals; Computer programming -- Periodicals; Computer programs -- Periodicals; 005.3
• DOI: 10.1002/spe.2518
• Languages: English
• ISSNs: 0038-0644

Abstract

Summary: Considering the constrained resources of mobile devices, a thorough performance evaluation of a mobile application is crucial. However, performance evaluation in the mobile domain is still a manual and time‐consuming task. The diversity of mobile devices only increases the complexity of this task. We propose EPE‐Mobile, a framework to automate early performance estimation in mobile applications. It is composed of a configurable library of basic operations and an engine that automatically creates a synthetic program based on the specification of a new app. The synthetic program that EPE‐Mobile generates provides feedback for mobile developers at the first design stages and before the actual implementation of a new application. The fast evaluation can also guide developers in optimizing their applications or in choosing devices with the best trade‐off between cost and performance to run a given application. Finally, developers can reuse the data collection infrastructure of the framework to collect performance data during all development stages. We validate the proposed framework using 4 applications from the Android Play Store. Based on their specifications, 4 synthetic programs were generated and executed on different devices. We compared the results to those obtained from the execution of the actual applications in the same devices. Experimental results show that it is possible to create synthetic applications with similar behavior to that of real applications and, thus, classify devices based on the actual application needs. The framework uses aspect‐oriented programming to collect the metrics of interest. This approach provides increased modularity and separation of concerns, thus facilitating the improvement of the framework itself, by adding other metrics or basic operations.