A Genetic Programming Approach to Automated Test Generation for Object-Oriented Software

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

@InProceedings{SeesingG06c,
  author =       "Arjan Seesing and Hans-Gerhard Gross",
  title =        "A Genetic Programming Approach to Automated Test
                 Generation for Object-Oriented Software",
  booktitle =    "Proceedings of the 1st International Workshop on
                 Evaluation of Novel Approaches to Software
                 Engineering",
  year =         "2006",
  keywords =     "genetic algorithms, genetic programming, SBSE",
  bibsource =    "http://crestweb.cs.ucl.ac.uk/resources/sbse_repository/repository.html",
  URL =          "http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.97.4413&rep=rep1&type=pdf",
  size =         "9 pages",
  abstract =     "In this article we propose a new method for creating
                 test software for object-oriented systems using a
                 genetic programming approach. We believe this approach
                 is advantageous over the more established search-based
                 test-case generation approaches because the test
                 software is represented and altered as a fully
                 functional computer program. Genetic programming (GP)
                 uses a tree-shaped data structure which is more
                 directly comparable and suitable for being mapped
                 instantly to abstract syntax trees commonly used in
                 computer languages and compilers. These structures can
                 be manipulated and executed directly, bypassing
                 intricate and error prone conversion procedures between
                 different representations. In addition, tree structures
                 make more operations possible, which keep the structure
                 and semantics of the evolving test software better
                 intact during program evolution, compared to linear
                 structures. This speeds up the evolutionary program
                 generation process because the loss of evolved
                 structures due to mutations and crossover is prevented
                 more effectively.",
  notes =        "Cf. \cite{SeesingG06b}",
}

Genetic Programming entries for Arjan Seesing Hans-Gerhard Gross

Citations