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.
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Notes
- 1.
Guava v18.0, Apache Collections v4.0.
- 2.
During development we discovered and fixed a bug in the hashCode implementation of the Node class in the com.github.javaparser.JavaParser library.
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Acknowledgement
Work funded by UK EPSRC grant EP/J017515/1. Data available at https://github.com/nburles/burles2015object.
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Burles, N., Bowles, E., Brownlee, A.E.I., Kocsis, Z.A., Swan, J., Veerapen, N. (2015). Object-Oriented Genetic Improvement for Improved Energy Consumption in Google Guava. In: Barros, M., Labiche, Y. (eds) Search-Based Software Engineering. SSBSE 2015. Lecture Notes in Computer Science(), vol 9275. Springer, Cham. https://doi.org/10.1007/978-3-319-22183-0_20
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