EvoTest Test Case Generation Using Genetic Programming and Software Analysis

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

@MastersThesis{Seesing:mastersthesis,
  author =       "Arjan Seesing",
  title =        "EvoTest Test Case Generation Using Genetic Programming
                 and Software Analysis",
  school =       "Electrical Engineering, Mathematics, and Computer
                 Science, Delft University of Technology",
  year =         "2006",
  address =      "The Netherlands",
  month =        "22 " # jun,
  keywords =     "genetic algorithms, genetic programming, SBSE",
  URL =          "http://swerl.tudelft.nl/bin/view/Main/ArjanSeesing",
  URL =          "http://swerl.tudelft.nl/twiki/pub/Main/ArjanSeesing/thesis_final_submitted.pdf",
  size =         "70 pages",
  abstract =     "Testing in diverse software development paradigms is
                 an ongoing problem in software engineering. Many
                 techniques have been devised over the past decades to
                 help software engineers create useful testing suites.
                 In this thesis, the focus is on test case generation
                 for object orientated software using genetic
                 programming. The automatic creation of test data is
                 still an open problem in object-oriented software
                 testing, and many new techniques are being researched.
                 For object orientated software, the automatic test data
                 generation technique is not sufficient, because besides
                 input data used for testing, it additionally has to
                 produce the right sequences of method calls, and the
                 right artefacts, to bring the object under test in the
                 required state for testing.

                 Genetic algorithms have already been used to tackle
                 typical testing problems with success, but the use of
                 genetic programming applied to automatic test case
                 generation is relatively new and promising. This master
                 thesis shows how genetic algorithms combined with
                 different types of software analysis can create new
                 unit tests with a high amount of program coverage.
                 Together with static analysis, the genetic algorithm is
                 able to generate tests for more real world programs in
                 a shorter amount of time. This new approach is
                 implemented in a prototype tool called EvoTest?. With
                 this tool I demonstrate the coverage obtained for small
                 programs and some larger real world programs.",
  notes =        "

                 supervisor: Hans-Gerhard Gross (TUDelft)

                 Georgia Institute of Technology, Alex Orso, Jim
                 Clause.

                 SERG The Software Engineering Research Group
                 http://swerl.tudelft.nl/bin/view/Main/SoftwareEngineeringResearchGroup",
}

Genetic Programming entries for Arjan Seesing

Citations