A Genetic Programming Approach to Automated Test Generation for Object-Oriented Software
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @Article{SeesingG06,
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author = "Arjan Seesing and Hans-Gerhard Gross",
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title = "A Genetic Programming Approach to Automated Test
Generation for Object-Oriented Software",
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journal = "International Transactions on System Science and
Applications",
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year = "2006",
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volume = "1",
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number = "2",
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pages = "127--134",
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keywords = "genetic algorithms, genetic programming",
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bibsource = "http://crestweb.cs.ucl.ac.uk/resources/sbse_repository/repository.html",
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URL = "http://www.st.ewi.tudelft.nl/~gross/Publications/Seesing_2006.pdf",
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size = "9 pages",
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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.",
- }
Genetic Programming entries for
Arjan Seesing
Hans-Gerhard Gross
Citations