- author = "Arjan Seesing and Hans-Gerhard Gross",
- title = "A Genetic Programming Approach to Automated Test Generation for Object-Oriented Software",
- journal = "International Transactions on Systems Science and Applications",
- volume = "1",
- number = "2",
- year = "2006",
- pages = "127--134",
- bibsource = "DBLP, http://dblp.uni-trier.de",
- month = sep,
- note = "Special Issue Section on Evaluation of Novel Approaches to Software Engineering Guest Editors: Pericles Loucopoulos and Kalle Lyytinen",
- keywords = "genetic algorithms, genetic programming, SBSE, search-based testing, test automation, object-oriented programming",
- ISSN = "1751-1461",
-
URL = "
http://siwn.org.uk/press/sai/itssa0001.htm",
- abstract = "This article proposes a new method for creating test software for object-oriented systems using a genetic programming approach. It is believed that 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 = "ITSSA http://siwn.org.uk/press/sai/itssa0001.htm
See also Technical Report TUD-SERG-2006-017 \cite{SeesingG06b}",
