Sixth International Conference on Quality Software (QSIC'06) Adaptive Random Testing with Enlarged Input Domain Beijing, China October 27-October 28 ISBN: 0-7695-2718-3
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/QSIC.2006.8
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.
Index Terms:
Random Testing, Adaptive Random Testing, test data selection
Citation:
Johannes Mayer, Christoph Schneckenburger, "Adaptive Random Testing with Enlarged Input Domain," qsic, pp.251-258, Sixth International Conference on Quality Software (QSIC'06), 2006 Usage of this product signifies your acceptance of the Terms of Use. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||