9th International Symposium on Search Based Software Engineering (SSBSE)
Doi
Abstract
Data structure selection and tuning is laborious but can vastly
improve application performance and memory footprint. In this paper,
we demonstrate how artemis, a multiobjective, cloud-based optimisation
framework can automatically find optimal, tuned data structures and how
it is used for optimising the Guava library. From the proposed solutions
that artemis found, 27.45% of them improve all measures (execution
time, CPU usage, and memory consumption). More specifically, artemis
managed to improve the memory consumption of Guava by up 13%,
execution time by up to 9%, and 4% CPU usage
