Object-Oriented Genetic Improvement for Improved Energy Consumption in Google Guava

  • Nathan Burles
  • Edward Bowles
  • Alexander E. I. Brownlee
  • Zoltan A. Kocsis
  • Jerry Swan
  • Nadarajen Veerapen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9275)

Abstract

In this work we use metaheuristic search to improve Google’s Guava library, finding a semantically equivalent version of com.google.common.collect.ImmutableMultimap with reduced energy consumption. Semantics-preserving transformations are found in the source code, using the principle of subtype polymorphism. We introduce a new tool, Opacitor, to deterministically measure the energy consumption, and find that a statistically significant reduction to Guava’s energy consumption is possible. We corroborate these results using Jalen, and evaluate the performance of the metaheuristic search compared to an exhaustive search—finding that the same result is achieved while requiring almost 200 times fewer fitness evaluations. Finally, we compare the metaheuristic search to an independent exhaustive search at each variation point, finding that the metaheuristic has superior performance.

Keywords

Genetic Improvement Object-oriented programming Subclass substitution Liskov Substitution Principle Energy profiling 

Notes

Acknowledgement

Work funded by UK EPSRC grant EP/J017515/1. Data available at https://github.com/nburles/burles2015object.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Nathan Burles
    • 1
  • Edward Bowles
    • 1
  • Alexander E. I. Brownlee
    • 2
  • Zoltan A. Kocsis
    • 2
  • Jerry Swan
    • 1
  • Nadarajen Veerapen
    • 2
  1. 1.University of YorkYorkUK
  2. 2.University of StirlingStirlingUK

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