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Empirical Evaluation of the Energy Impact of Refactoring Code Smells

Authors: Verdecchia R.; Saez R. A.; Procaccianti G.; Lago P.;

Empirical Evaluation of the Energy Impact of Refactoring Code Smells

Abstract

Software energy efficiency has gained increasing attention of the research community. How to improve it, however, still lacks evidence. Specifically, the impact of code smell refactoring on energy efficiency has been scarcely investigated. In the pilot study here reported, we investigate the impact on performance and energy consumption of refactoring well-known code smells on Java software applications. In order to understand if software metrics can be used as indicators of the energy impact of refactoring, we also measured the variation caused by refactoring on a set of well-established software metrics. We conducted a controlled experiment using state-of-the-art power measurement equipment. Statistical hypothesis testing and effect size estimation were performed on the experimental results. Results show that in one out of three applications, refactoring each smell significantly impacted power- and energy consumption. Specifically, refactoring Feature Envy and Long Method smells led to a 49% energy efficiency improvement. No software metrics significantly correlated with execution time, power or energy consumption. In conclusion, refactoring code smells can significantly improve software energy efficiency. The magnitude of the impact may depend on application properties, e.g. size or age. Further research is needed to understand the relationship between software metrics and energy efficiency.

Countries
Netherlands, Italy, Netherlands
Keywords

Software Engineering, sustainability, Green IT, Connected World, SDG 7 - Affordable and Clean Energy, Science for Sustainability, Empirical Software Engineering, Energy Efficiency, Code Smells, energy efficiency

  • BIP!
    Impact byBIP!
    citations
    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).
    19
    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.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Top 10%
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 10%
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citations
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!
19
Top 10%
Top 10%
Top 10%
bronze