Evolving Rules for Action Selection in Automated Testing via Genetic Programming - A First Approach

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

  author =       "Anna I. Esparcia-Alcazar and Francisco Almenar and 
                 Urko Rueda and Tanja E. J. Vos",
  title =        "Evolving Rules for Action Selection in Automated
                 Testing via Genetic Programming - A First Approach",
  booktitle =    "20th European Conference on the Applications of
                 Evolutionary Computation",
  year =         "2017",
  editor =       "Giovanni Squillero",
  series =       "LNCS",
  volume =       "10200",
  publisher =    "Springer",
  pages =        "82--95",
  address =      "Amsterdam",
  month =        "19-21 " # apr,
  organisation = "Species",
  keywords =     "genetic algorithms, genetic programming, Automated
                 testing via the GUI, Action selection for testing,
                 Testing metrics",
  DOI =          "doi:10.1007/978-3-319-55792-2_6",
  abstract =     "Tools that perform automated software testing via the
                 user interface rely on an action selection mechanism
                 that at each step of the testing process decides what
                 to do next. This mechanism is often based on random
                 choice, a practice commonly referred to as monkey
                 testing. In this work we evaluate a first approach to
                 genetic programming (GP) for action selection that
                 involves evolving IF-THEN-ELSE rules; we carry out
                 experiments and compare the results with those obtained
                 by random selection and also by -learning, a
                 reinforcement learning technique. Three applications
                 are used as Software Under Test (SUT) in the
                 experiments, two of which are proprietary desktop
                 applications and the other one an open source web-based
                 application. Statistical analysis is used to compare
                 the three action selection techniques on the three
                 SUTs; for this, a number of metrics are used that are
                 valid even under the assumption that access to the
                 source code is not available and testing is only
                 possible via the GUI. Even at this preliminary stage,
                 the analysis shows the potential of GP to evolve action
                 selection mechanisms.",
  notes =        "EvoApplications2017 held in conjunction with
                 EuroGP'2017, EvoCOP2017 and EvoMusArt2017

Genetic Programming entries for Anna Esparcia-Alcazar Francisco Almenar Urko Rueda Tanja E J Vos