Adaptive Random Testing with Enlarged Input Domain

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