|Appears in Collections:||Computing Science and Mathematics Conference Papers and Proceedings|
|Peer Review Status:||Refereed|
Attila, Kocsis Zoltan
|Title:||Object-Oriented Genetic Improvement for Improved Energy Consumption in Google Guava|
|Citation:||Burles N, Bowles E, Brownlee A, Attila Kocsis Z, Swan J & Veerapen N (2015) Object-Oriented Genetic Improvement for Improved Energy Consumption in Google Guava In: Barros M, Labiche Y (ed.) Search-Based Software Engineering, Switzerland: Springer International Publishing. Symposium on Search-Based Software Engineering (SSBSE 2015), 5.9.2015 - 7.9.2015, Bergamo, Italy, pp. 255-261.|
|Series/Report no.:||Lecture Notes in Computer Science, 9275|
|Conference Name:||Symposium on Search-Based Software Engineering (SSBSE 2015)|
|Conference Location:||Bergamo, Italy|
|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.|
|Status:||Book Chapter: author post-print (pre-copy editing)|
|Rights:||This is the author-created version. The final publication is available at http://link.springer.com/chapter/10.1007/978-3-319-22183-0_20 Publisher policy allows this work to be made available in this repository. Published in Search-Based Software Engineering, Lecture Notes in Computing Science 9275.|
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