A Novel Approach to Generating Test Cases with Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4192

  author =       "Saso Karakatic and Tina Schweighofer",
  title =        "A Novel Approach to Generating Test Cases with Genetic
  booktitle =    "Proceedings of the 10th International Conference on
                 Knowledge Management in Organizations, KMO 2015",
  year =         "2015",
  editor =       "Lorna Uden and Marjan Hericko and I-Hsien Ting",
  volume =       "224",
  series =       "Lecture Notes in Business Information Processing",
  pages =        "260--271",
  address =      "Maribor, Slovenia",
  month =        aug # " 24-28",
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming, Software
                 testing, Activity diagram, UML, Test cases",
  isbn13 =       "978-3-319-21008-7",
  URL =          "http://dx.doi.org/10.1007/978-3-319-21009-4_20",
  DOI =          "doi:10.1007/978-3-319-21009-4_20",
  abstract =     "Part of the automating software testing procedure
                 includes the automation of test cases. Automation can
                 lower the cost and effort and at the same time can
                 increase the quality of test cases and consequently the
                 testing procedure. Many different approaches for test
                 case generation are available: generation from code,
                 formal methods and different models, among others also
                 from UML diagrams, more precisely from UML activity
                 diagrams. Researchers use different techniques, of
                 which genetic programming (GP) is very popular and was
                 used in our research. In the proposed approach we
                 generated test cases from the UML activity diagram,
                 from which we constructed the binary decision tree
                 structure, which is used as an instance in the
                 evolution process of GP. The default tree structure is
                 used throughout the whole evolution process, only the
                 content (the testing parameters) of the nodes changes.
                 The process of evolution consists of several genetic
                 operators, such as selection, crossover and mutation.
                 The main novelty of our method is a different fitness
                 function than we can find in existing literature. In
                 contrast to related work where the coverage is used -
                 we used the error occurrence for our metric. The
                 proposed method is demonstrated on the example of an
                 automated teller machine (ATM), where we show how the
                 full automation of test case generation and testing is
                 a major advantage of our method.",
  notes =        "Faculty of Electrical Engineering and Computer
                 Science, University of Maribor, Smetanova 17, 2000,
                 Maribor, Slovenia",

Genetic Programming entries for Saso Karakatic Tina Schweighofer