Search-Based Temporal Testing of Multicore Applications
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @PhdThesis{Srivisut:thesis,
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author = "Komsan Srivisut",
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title = "Search-Based Temporal Testing of Multicore
Applications",
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school = "Computer Science, The University of York",
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year = "2017",
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address = "UK",
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month = sep,
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keywords = "genetic algorithms, genetic programming, SBSE, ECJ,
P4080",
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URL = "http://etheses.whiterose.ac.uk/22102/1/Komsan2017.pdf",
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URL = "http://etheses.whiterose.ac.uk/id/eprint/22102",
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size = "262 pages",
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abstract = "Multicore systems are increasingly common as a modern
computing platform. Multicore processors not only offer
better performance-to-cost ratios relative to
single-core processors but also have significantly
minimised space, weight, and power (SWaP) constraints.
Unfortunately, they introduce challenges in
verification as their shared components are potential
channels for interference. The potential for
interference increases the possibility of concurrency
faults at runtime and consequently increases the
difficulty of verifying. In this thesis, search-based
techniques are empirically investigated to determine
their effectiveness in temporal testing-searching for
test inputs that may lead a task running on an embedded
multicore to produce extreme (here longest) execution
times, which might cause the system to violate its
temporal requirements. Overall, the findings suggest
that various forms of search-based approaches are
effective in generating test inputs exhibiting extreme
execution times on the embedded multicore environment.
All previous work in temporal testing has evolved test
data directly; this is not essential. In this thesis,
one novel proposed approach, i.e. the use of search to
discover high performing biased random sampling regimes
(which we call dependent input sampling strategies),
has proved particularly effective. Shifting the target
of search from test data itself to strategies proves
particularly well motivated for attaining extreme
execution times. Finally, we present also preliminary
results on the use of so-called hyper-heuristics, which
can be used to form optimal hybrids of optimisation
techniques. An extensive comparison of direct
approaches to establishing a baseline is followed by
reports of research into indirect approaches and
hyper-heuristics. The shift to strategies from direct
data can be thought of as a leap in abstraction level
for the underlying temporal test data generation
problem. The shift to hyper-heuristics aims to boost
the level of optimisation technique abstraction. The
former is more fully worked out than the latter and has
proved a significant success. For the latter only
preliminary results are available; as will be seen from
this work as the whole computational requirements for
research experimentation are significant.",
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notes = "supervisors: John A Clark and Richard F Paige",
- }
Genetic Programming entries for
Komsan Srivisut
Citations