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@InProceedings{YanoMartSous:2010:GeFeTe,
               author = "Yano, T. and Martins, E. and Sousa, F. L.",
          affiliation = "Institute of Computing, State University of Campinas, UNICAMP, 
                         Campinas, SP, Brazil and Institute of Computing, State University 
                         of Campinas, UNICAMP, Campinas, SP, Brazil and {Instituto Nacional 
                         de Pesquisas Espaciais (INPE)}",
                title = "Generating feasible test paths from an executable model using a 
                         multi-objective approach",
                 year = "2010",
                pages = "236--239",
         organization = "3rd International Conference on Software Testing, Verification, 
                         and Validation Workshops, 3. (ICSTW)",
             keywords = "Executable model, Feasible path, Model-based testing, 
                         Multi-objective optimization, Behavior model, Evolutionary 
                         approach, Executable model, Extended finite state machine, 
                         Infeasible paths, Meta heuristics, Multi objective, Open problems, 
                         Path models, Size minimization, Test data generation, Test 
                         purpose, Test sequence, Testing technique, White-box testing.",
             abstract = "Search-based testing techniques using metaheuristics, like 
                         evolutionary algorithms, has been largely used for test data 
                         generation, but most approaches were proposed for white-box 
                         testing. In this paper we present an evolutionary approach for 
                         test sequence generation from a behavior model, in particular, 
                         Extended Finite State Machine. An open problem is the production 
                         of infeasible paths, as these should be detected and discarded 
                         manually. To circumvent this problem, we use an executable model 
                         to obtain feasible paths dynamically. An evolutionary algorithm is 
                         used to search for solutions that cover a given test purpose, 
                         which is a transition of interest. The target transition is used 
                         as a criterion to get slicing information, in this way, helping to 
                         identify the parts of the model that affect the test purpose. We 
                         also present a multi-objective search: the test purpose coverage 
                         and the sequence size minimization, as longer sequences require 
                         more effort to be executed.",
  conference-location = "Paris",
      conference-year = "6 - 10 Apr. 2010",
                  doi = "10.1109/ICSTW.2010.52",
                  url = "http://dx.doi.org/10.1109/ICSTW.2010.52",
                 isbn = "978-076954050-4",
             language = "en",
           targetfile = "Yano_Generation.pdf",
               volume = "Article number 5463651",
        urlaccessdate = "17 jan. 2021"
}


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