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https://doi.org/10.1145/374491...
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On the Effect of Feature Reduction on Energy Consumption: An Exploratory Study

Authors: Xhevahire Tërnava; Romain Lefeuvre; Quentin Perez; Djamel Eddine Khelladi; Mathieu Acher; Benoit Combemale;

On the Effect of Feature Reduction on Energy Consumption: An Exploratory Study

Abstract

Energy consumption is a growing concern for sustainable software. Although increasingly studied, it remains largely unexplored in configurable systems growing in complexity with features. Feature reduction can eliminate software bloat, but to our knowledge, its impact on energy use has not been investigated. To fill this gap, we investigated how both on-demand and built-in feature reduction (defined later) affect the energy consumption of configurable systems. We conducted a first exploratory study using 28 programs from three systems with built-in feature reduction, namely ToyBox, BusyBox, and GNU, as well as 6 GNU programs debloated on-demand using the Chisel, Debop, and Cov tools. In our results, built-in feature reduction led to statistically significant energy decreases in 7% of the cases, while on-demand reduction, despite achieving energy decreases in 67% of cases, showed no statistical significance. However, when energy consumption increased, it was often more substantial than the reductions observed (occurring in 25% of built-in cases and 11% of on-demand cases) showing the complex and sometimes counterintuitive interplay between feature reduction and energy. Additionally, the observed strong correlation between energy consumption and execution time motivates a shift from traditional debloating goals, centered on binary size/attack surface, to energy-aware strategies that prioritize performance concerns. Finally, we provide an in-depth analysis and discuss the perspective.

Keywords

Energy consumption, Configurable systems, [INFO.INFO-SE] Computer Science [cs]/Software Engineering [cs.SE], Feature reduction

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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!
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