Reference : Stress Testing of Task Deadlines: A Constraint Programming Approach
Scientific congresses, symposiums and conference proceedings : Paper published in a book
Engineering, computing & technology : Computer science
http://hdl.handle.net/10993/4901
Stress Testing of Task Deadlines: A Constraint Programming Approach
English
Di Alesio, Stefano mailto [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC) >]
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)]
Gotlieb, Arnaud mailto [Simula Research Lab]
2013
The 24th IEEE International Symposium on Software Reliability Engineering (ISSRE 2013), Pasadena, CA, November 2013
Yes
The 24th IEEE International Symposium on Software Reliability Engineering (ISSRE 2013)
November 2013
[en] Safety-critical Real Time Embedded Systems (RTESs)
are usually subject to strict timing and performance requirements
that must be satisfied for the system to be deemed safe. In this
paper, we use effective search strategies that aim at finding worst
case scenarios with respect to deadline misses. Such scenarios can
in turn be used to test the target RTES and ensure that, even
under worst case conditions, it satisfies its timing requirements.
Specifically, we develop a solution based on Constraint Programming
(CP) to automate the generation of test cases that reveal,
or are likely to, task deadline misses. We evaluate it through
a comparison with a recent, state-of-the-art approach based on
Genetic Algorithms (GA). In particular, we compare CP and GA
in five industry-inspired case studies for efficiency, effectiveness,
and scalability. Our experimental results show that, on the largest
and more complex case studies, CP performs significantly better
than GA. Since CP has interesting properties, such as guaranteeing
complete search when there is sufficient time, and enables the
definition of effective heuristics to converge faster towards optimal
solutions, we conclude that our results are encouraging and suggest
this is an advantageous solution for the stress testing of RTESs with
respect to timing constraints.
http://hdl.handle.net/10993/4901

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