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image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Empirical Software E...arrow_drop_down
image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
Empirical Software Engineering
Article . 2021 . Peer-reviewed
License: Springer TDM
Data sources: Crossref
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Improving energy-efficiency by recommending Java collections

Authors: Wellington Oliveira; Renato Oliveira; Fernando Castor; Gustavo Pinto; João Paulo Fernandes;

Improving energy-efficiency by recommending Java collections

Abstract

Over the last years, increasing attention has been given to creating energy-efficient software systems. However, developers still lack the knowledge and the tools to support them in that task. In this work, we explore our vision that non-specialists can build software that consumes less energy by alternating diversely-designed pieces of software without increasing the development complexity. To support our vision, we propose an approach for energy-aware development that combines the construction of application-independent energy profiles of Java collections and static analysis to produce an estimate of in which ways and how intensively a system employs these collections. We implement this approach in a tool named CT+ that works with both desktop and mobile Java systems and is capable of analyzing 39 different collection implementations of lists, maps, and sets. We applied CT+ to seventeen software systems: two mobile-based, twelve desktop-based, and three that can run in both environments. Our evaluation infrastructure involved a high-end server, two notebooks, three smartphones, and a tablet. Overall, 2295 recommendations were applied, achieving up to 16.34% reduction in energy consumption, usually changing a single line of code per recommendation. Even for a real-world, mature system such as Tomcat, CT+ could achieve a 4.12% reduction in energy consumption. Our results indicate that some widely used collections, e.g., ArrayList, HashMap, and Hashtable, are not energy- efficient and sometimes should be avoided when energy consumption is a major concern.

Keywords

Energy consumption, Collections, Recommendation systems

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
13
Top 10%
Average
Top 10%
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