Reference : Identifying Optimal Trade-Offs between CPU Time Usage and Temporal Constraints Using ...
Scientific congresses, symposiums and conference proceedings : Paper published in a book
Engineering, computing & technology : Computer science
http://hdl.handle.net/10993/16336
Identifying Optimal Trade-Offs between CPU Time Usage and Temporal Constraints Using Search
English
Nejati, Shiva mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Briand, Lionel mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > > ; University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC)]
Jul-2014
International Symposium on Software Testing and Analysis (ISSTA 2014)
11
Yes
International Symposium on Software Testing and Analysis (ISSTA 2014)
July 21-25, 2014
[en] Integration of software from different sources is a critical activity in
many embedded systems across most industry sectors. Software integrators
are responsible for producing reliable systems that fulfill
various functional and performance requirements. In many situations,
these requirements inversely impact one another. In particular,
embedded system integrators often need to make compromises
regarding some of the functional system properties to optimize the
use of various resources, such as CPU time. In this paper, motivated
by challenges faced by industry, we introduce a multi-objective decision
support approach to help balance the minimization of CPU
time usage and the satisfaction of temporal constraints in automotive
systems. We develop a multi-objective, search-based optimization
algorithm, specifically designed to work for large search
spaces, to identify optimal trade-off solutions fulfilling these two
objectives. We evaluated our algorithm by applying it to a large automotive
system. Our results show that our algorithm can find solutions
that are very close to the estimated ideal optimal values, and
further, it finds significantly better solutions than a random strategy
while being faster. Finally, our approach efficiently identifies
a large number of diverse solutions, helping domain experts and
other stakeholders negotiate the solutions to reach an agreement.
http://hdl.handle.net/10993/16336

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