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2012 IEEE Fifth International Conference on Software Testing, Verification and Validation
Generating String Test Data for Code Coverage
Montreal, Quebec Canada
April 17-April 21
ISBN: 978-0-7695-4670-4
| ASCII Text | x | ||
| Michael Beyene, James H. Andrews, "Generating String Test Data for Code Coverage," Software Testing, Verification, and Validation, 2008 International Conference on, pp. 270-279, 2012 IEEE Fifth International Conference on Software Testing, Verification and Validation, 2012. | |||
| BibTex | x | ||
| @article{ 10.1109/ICST.2012.107, author = {Michael Beyene and James H. Andrews}, title = {Generating String Test Data for Code Coverage}, journal ={Software Testing, Verification, and Validation, 2008 International Conference on}, volume = {0}, year = {2012}, isbn = {978-0-7695-4670-4}, pages = {270-279}, doi = {http://doi.ieeecomputersociety.org/10.1109/ICST.2012.107}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, } | |||
| RefWorks Procite/RefMan/Endnote | x | ||
| TY - CONF JO - Software Testing, Verification, and Validation, 2008 International Conference on TI - Generating String Test Data for Code Coverage SN - 978-0-7695-4670-4 SP270 EP279 A1 - Michael Beyene, A1 - James H. Andrews, PY - 2012 KW - Software testing KW - string data generation KW - metaheuristic algorithms KW - structural code coverage VL - 0 JA - Software Testing, Verification, and Validation, 2008 International Conference on ER - | |||
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICST.2012.107
String data has traditionally been difficult for test data generation tools to generate. Of particular concern are strings that conform to a given grammar, since coverage of grammar productions does not guarantee high code coverage or fault-finding ability. We address this problem by deriving Java classes from the grammatical categories in the grammar, and then using a collection of deterministic and met heuristic techniques to generate strings from them. We compare the code coverage of these techniques and the standard conformance test suites for the grammars. We conclude that the various techniques have complementary strengths, and that they can be usefully used in combination.
Index Terms:
Software testing, string data generation, metaheuristic algorithms, structural code coverage
Citation:
Michael Beyene, James H. Andrews, "Generating String Test Data for Code Coverage," icst, pp.270-279, 2012 IEEE Fifth International Conference on Software Testing, Verification and Validation, 2012
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