A Genetic Programming Approach to Automated Test Generation for Object Oriented Software
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @TechReport{SeesingG06b,
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author = "Hans-Gerhard Gross and Arjan Seesing",
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author2 = "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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institution = "Department of Software Technology, Faculty of
Electrical Engineering, Mathematics and Computer
Science, Delft University of Technology",
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year = "2006",
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type = "Software Engineering Research Group, Technical Report
Series",
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number = "TUD-SERG-2006-017",
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address = "Mekelweg 4, 2628 CD Delft, The Netherlands",
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bibsource = "http://crestweb.cs.ucl.ac.uk/resources/sbse_repository/repository.html",
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keywords = "genetic algorithms, genetic programming, SBSE",
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URL = "http://www.st.ewi.tudelft.nl/~gross/Publications/TUD-SERG-2006-017.pdf",
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URL = "http://swerl.tudelft.nl/twiki/pub/Main/TechnicalReports/TUD-SERG-2006-017.pdf",
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ISSN = "1872-5392",
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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.",
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notes = "Doubt about order of authors. See also
\cite{DBLP:journals/itssa/SeesingG06}",
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size = "20 pages",
- }
Genetic Programming entries for
Hans-Gerhard Gross
Arjan Seesing
Citations