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Statically Analyzing the Energy Efficiency of Software Product Lines

by 1,‡, 2,‡ and 1,*,‡
1
HASLab/INESC TEC & Department of Informatics, Universidade do Minho, 4710-057 Braga, Portugal
2
Departamento de Engenharia Informática, Faculdade de Engenharia, Universidade do Porto, 4099-002 Porto, Portugal
*
Author to whom correspondence should be addressed.
This paper is an extended version of our paper published in SPLC: Marco Couto, Paulo Borba, Jácome Cunha, João Paulo Fernandes, Rui Pereira, and João Saraiva. 2017. Products go Green: Worst-Case Energy Consumption in Software Product Lines. In Proceedings of the 21st International Systems and Software Product Line Conference—Volume A (SPLC ’17). Association for Computing Machinery, New York, NY, USA, 84–93. doi:10.1145/3106195.3106214.
The authors contributed equally to this work.
Academic Editor: Andrea Acquaviva
J. Low Power Electron. Appl. 2021, 11(1), 13; https://0-doi-org.brum.beds.ac.uk/10.3390/jlpea11010013
Received: 31 January 2021 / Revised: 1 March 2021 / Accepted: 15 March 2021 / Published: 23 March 2021
(This article belongs to the Special Issue Energy-Efficient Embedded Computing)
Optimizing software to become (more) energy efficient is an important concern for the software industry. Although several techniques have been proposed to measure energy consumption within software engineering, little work has specifically addressed Software Product Lines (SPLs). SPLs are a widely used software development approach, where the core concept is to study the systematic development of products that can be deployed in a variable way, e.g., to include different features for different clients. The traditional approach for measuring energy consumption in SPLs is to generate and individually measure all products, which, given their large number, is impractical. We present a technique, implemented in a tool, to statically estimate the worst-case energy consumption for SPLs. The goal is to reason about energy consumption in all products of a SPL, without having to individually analyze each product. Our technique combines static analysis and worst-case prediction with energy consumption analysis, in order to analyze products in a feature-sensitive manner: a feature that is used in several products is analyzed only once, while the energy consumption is estimated once per product. This paper describes not only our previous work on worst-case prediction, for comprehensibility, but also a significant extension of such work. This extension has been realized in two different axis: firstly, we incorporated in our methodology a simulated annealing algorithm to improve our worst-case energy consumption estimation. Secondly, we evaluated our new approach in four real-world SPLs, containing a total of 99 software products. Our new results show that our technique is able to estimate the worst-case energy consumption with a mean error percentage of 17.3% and standard deviation of 11.2%. View Full-Text
Keywords: energy estimation; program analysis; software product lines energy estimation; program analysis; software product lines
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MDPI and ACS Style

Couto, M.; Fernandes, J.P.; Saraiva, J. Statically Analyzing the Energy Efficiency of Software Product Lines. J. Low Power Electron. Appl. 2021, 11, 13. https://0-doi-org.brum.beds.ac.uk/10.3390/jlpea11010013

AMA Style

Couto M, Fernandes JP, Saraiva J. Statically Analyzing the Energy Efficiency of Software Product Lines. Journal of Low Power Electronics and Applications. 2021; 11(1):13. https://0-doi-org.brum.beds.ac.uk/10.3390/jlpea11010013

Chicago/Turabian Style

Couto, Marco, João P. Fernandes, and João Saraiva. 2021. "Statically Analyzing the Energy Efficiency of Software Product Lines" Journal of Low Power Electronics and Applications 11, no. 1: 13. https://0-doi-org.brum.beds.ac.uk/10.3390/jlpea11010013

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