GPTesT: A Testing Tool Based On Genetic Programming

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

  author =       "Maria Cl{\'a}udia Figueiredo Pereira Emer and 
                 Silvia Regina Vergilio",
  title =        "{GPTesT}: {A} Testing Tool Based On Genetic
  booktitle =    "GECCO 2002: Proceedings of the Genetic and
                 Evolutionary Computation Conference",
  editor =       "W. B. Langdon and E. Cant{\'u}-Paz and K. Mathias and 
                 R. Roy and D. Davis and R. Poli and K. Balakrishnan and 
                 V. Honavar and G. Rudolph and J. Wegener and 
                 L. Bull and M. A. Potter and A. C. Schultz and J. F. Miller and 
                 E. Burke and N. Jonoska",
  year =         "2002",
  pages =        "1343--1350",
  address =      "New York",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "9-13 " # jul,
  publisher =    "Morgan Kaufmann Publishers",
  keywords =     "genetic algorithms, genetic programming, search-based
                 software engineering, fault-based testing, induction of
                 programs, mutation analysis, software test criteria",
  ISBN =         "1-55860-878-8",
  URL =          "",
  URL =          "",
  URL =          "",
  abstract =     "Genetic Programming (GP) has recently been applied to
                 solve problems in several areas. It has the goal of
                 inducing programs from test cases by using the concepts
                 of Darwin's evolution theory. On the other hand,
                 software testing, that is a fundamental and expensive
                 activity for software quality assurance, has the
                 objective of generating test cases from the program
                 being tested. In this sense, a symmetry between
                 induction of programs based on GP and testing is
                 noticed. Based on such symmetry, this work presents
                 GPTesT, a testing tool based on GP. Fault-based testing
                 criteria generally derive test data using a set of
                 mutant operators to produce alternatives that differ
                 from the program under testing by a simple
                 modification. GPtesT uses a set of alternatives
                 genetically derived, which allow the test of
                 interactions between faults. GPTesT implements two test
                 procedures respectively for guiding the selection and
                 evaluation of test data sets. Examples with these
                 procedures show that the approach can be used as a
                 testing criterion.",
  notes =        "GECCO-2002. A joint meeting of the eleventh
                 International Conference on Genetic Algorithms
                 (ICGA-2002) and the seventh Annual Genetic Programming
                 Conference (GP-2002)",

Genetic Programming entries for Maria Claudia Figueiredo Pereira Emer Silvia Regina Vergilio