DocumentCode :
2929562
Title :
Adaptive Random Testing with Enlarged Input Domain
Author :
Mayer, Johannes ; Schneckenburger, Christoph
Author_Institution :
Dept. of Appl. Inf. Process., Ulm Univ.
fYear :
2006
fDate :
27-28 Oct. 2006
Firstpage :
251
Lastpage :
258
Abstract :
Adaptive random testing (ART) subsumes a family of random testing techniques that are designed to be more effective than pure random testing. These methods spread test cases more evenly within the input domain than a uniform distribution does. In the present paper, it is investigated why standard ART methods are less effective for higher failure rates. Therefore, the spatial distribution of the test cases generated by these methods is analyzed - also in higher dimensions - with a new approach. Based on the results of the analysis, improved algorithms are proposed that are equally effective for all failure rates as an empirical study reveals
Keywords :
failure analysis; program testing; software fault tolerance; adaptive random testing; spatial distribution; test data selection; Algorithm design and analysis; Automatic testing; Failure analysis; Information processing; Performance evaluation; Software measurement; Software quality; Software testing; Subspace constraints; System testing; Adaptive Random Testing; Random Testing; test data selection;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Quality Software, 2006. QSIC 2006. Sixth International Conference on
Conference_Location :
Beijing
ISSN :
1550-6002
Print_ISBN :
0-7695-2718-3
Type :
conf
DOI :
10.1109/QSIC.2006.8
Filename :
4032292
Link To Document :
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