Signal Generation for Search-Based Testing of Continuous Systems

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{Windisch:2009:SBST,
  author =       "Andreas Windisch and Noura {Al Moubayed}",
  title =        "Signal Generation for Search-Based Testing of
                 Continuous Systems",
  booktitle =    "2nd International Workshop on Search-Based Software
                 Testing",
  year =         "2009",
  editor =       "Phil McMinn and Robert Feldt",
  address =      "Denver, Colorado, USA",
  month =        "1 " # apr,
  organisation = "EvoTest",
  keywords =     "genetic algorithms, genetic programming, linear
                 genetic programming, PSO, SBSE",
  URL =          "http://iaser.tek.bth.se/feldt/conferences/sbst09/papers/windisch_sbst09.pdf",
  size =         "10 pages",
  abstract =     "Test case generation constitutes a critical activity
                 in software testing that is cost-intensive,
                 time-consuming and error-prone when done manually.
                 Hence, an automation of this process is required. One
                 automation approach is search-based testing for which
                 the task of generating test data is transformed into an
                 optimization problem which is solved using
                 metaheuristic search techniques. However, only little
                 work has been done so far applying search-based testing
                 techniques to systems that depend on continuous input
                 signals.

                 This paper proposes two novel approaches to generating
                 input signals from within search-based testing
                 techniques for continuous systems. These approaches are
                 then shown to be very effective when experimentally
                 applied to the problem of approximating a set of
                 realistic signals.",
  notes =        "Fourier analysis (optimised by PSO) or GP combination
                 of sine, spline, linear, step and impulse. Multiple
                 chromosomes per individual. Homologous crossover, two
                 point crossover. Reducing mutation: removes genes from
                 chromosomes (cf also extending mutation: add genes to
                 end of chromosome). Multi-point mutation. Reinsertion:
                 add mutants to population. 6 training data from
                 Mercedes Benz cars, logged by CAN bus 30secs at
                 1khz.

                 sec 3.3 {"}small impulse-like steps do not carry much
                 weight{"}. Fourier series unable to reproduce constant
                 (wrong parameters chosen?). {"}slight advantage for the
                 linear genetic programming approach{"}. EvoTest.
                 http://iaser.tek.bth.se/feldt/conferences/sbst09 In
                 conjunction with ICST 2009 IEEE International
                 Conference on Testing, Verification and Validation",
}

Genetic Programming entries for Andreas Windisch Noura Al Moubayed

Citations