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Title: Search-based temporal testing of multicore applications
Author: Srivisut, Komsan
ISNI:       0000 0004 7431 896X
Awarding Body: University of York
Current Institution: University of York
Date of Award: 2017
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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.
Supervisor: Paige, Richard Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available