A strategy for evaluating feasible and unfeasible test cases for the evolutionary testing of object-oriented software

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{Bregieiro-Ribeiro:2008:AST,
  author =       "Jose Carlos {Bregieiro Ribeiro} and 
                 Mario Alberto Zenha-Rela and Francisco {Fernandez de Vega}",
  title =        "A strategy for evaluating feasible and unfeasible test
                 cases for the evolutionary testing of object-oriented
                 software",
  booktitle =    "AST '08: Proceedings of the 3rd international workshop
                 on Automation of software test",
  year =         "2008",
  pages =        "85--92",
  address =      "Leipzig, Germany",
  publisher_address = "New York, NY, USA",
  publisher =    "ACM",
  keywords =     "genetic algorithms, genetic programming, SBSE,
                 Search-Based Test Case Generation, Evolutionary
                 Testing, Object-Orientation, Strongly-Typed Genetic
                 Programming, Software Engineering, Testing and
                 Debugging| Testing tools, Verification",
  isbn13 =       "978-1-60558-030-2",
  URL =          "http://jcbribeiro.googlepages.com/ast12-ribeiro.pdf",
  DOI =          "doi:10.1145/1370042.1370061",
  size =         "8 pages",
  abstract =     "Evolutionary Testing is an emerging methodology for
                 automatically producing high quality test data. The
                 focus of our on-going work is precisely on generating
                 test data for the structural unit-testing of
                 object-oriented Java programs. The primary objective is
                 that of efficiently guiding the search process towards
                 the definition of a test set that achieves full
                 structural coverage of the test object.

                 However, the state problem of object-oriented programs
                 requires specifying carefully ne-tuned methodologies
                 that promote the traversal of problematic structures
                 and difficult controlflow paths - which often involves
                 the generation of complex and intricate test cases,
                 that dene elaborate state scenarios.

                 This paper proposes a methodology for evaluating the
                 quality of both feasible and unfeasible test cases -
                 i.e., those that are effectively completed and
                 terminate with a call to the method under test, and
                 those that abort prematurely because a runtime
                 exception is thrown during test case execution. With
                 our approach, unfeasible test cases are considered at
                 certain stages of the evolutionary search, promoting
                 diversity and enhancing the possibility of achieving
                 full coverage.",
  notes =        "also known as \cite{1370061}",
}

Genetic Programming entries for Jose Carlos Bregieiro Ribeiro Mario Alberto Zenha-Rela Francisco Fernandez de Vega

Citations