Search-based test data generation from stateflow statecharts

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

@InProceedings{Windisch:2010:gecco,
  author =       "Andreas Windisch",
  title =        "Search-based test data generation from stateflow
                 statecharts",
  booktitle =    "GECCO '10: Proceedings of the 12th annual conference
                 on Genetic and evolutionary computation",
  year =         "2010",
  editor =       "Juergen Branke and Martin Pelikan and Enrique Alba and 
                 Dirk V. Arnold and Josh Bongard and 
                 Anthony Brabazon and Juergen Branke and Martin V. Butz and 
                 Jeff Clune and Myra Cohen and Kalyanmoy Deb and 
                 Andries P Engelbrecht and Natalio Krasnogor and 
                 Julian F. Miller and Michael O'Neill and Kumara Sastry and 
                 Dirk Thierens and Jano {van Hemert} and Leonardo Vanneschi and 
                 Carsten Witt",
  isbn13 =       "978-1-4503-0072-8",
  pages =        "1349--1356",
  keywords =     "genetic algorithms, genetic programming, Testing,
                 Debugging, SBSE, Search-based software engineering",
  month =        "7-11 " # jul,
  organisation = "SIGEVO",
  address =      "Portland, Oregon, USA",
  DOI =          "doi:10.1145/1830483.1830732",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "This paper proposes an effective method for automating
                 the test data generation process aiming at structurally
                 covering Stateflow statecharts, while assuring the
                 generation of suitable and - most notably - realistic
                 and meaningful system inputs. For this purpose the
                 principles of evolutionary structural testing have been
                 adapted both for the application to state charts and
                 for the consideration of continuous signals. The
                 approach is evaluated using a complex industrial case
                 study in comparison to random testing. The results
                 demonstrate the value of this approach in industrial
                 settings due to both its search effectiveness and its
                 high degree of automation, potentially contributing to
                 an improvement in quality assurance of embedded
                 software systems.",
  notes =        "Simulink, Matlab. Two examples: 1) automatic road
                 vehicle transmission controller mode 2) real industrial
                 (proprietary) model of a windscreen wiper controller
                 (core logics) taken from a Mercedes-Benz development
                 project.

                 Also known as \cite{1830732} GECCO-2010 A joint meeting
                 of the nineteenth international conference on genetic
                 algorithms (ICGA-2010) and the fifteenth annual genetic
                 programming conference (GP-2010)",
}

Genetic Programming entries for Andreas Windisch

Citations