A revisit of adaptive random testing by restriction

Abstract

Adaptive random testing is a black box testing method based on the intuition that random testing failure-finding efficiency can be improved upon, in certain situations, by ensuring a more widespread and evenly distributed spread of test cases in the input domain. One way of achieving this distribution is through the use of exclusion zones and restriction, resulting in a method called restricted random testing (RRT). Recent investigations into the RRT method have revealed several interesting and significant insights. A method of reducing the computational overheads of testing methods by partitioning an input domain, and applying the method to only one of the subdomains, mapping the test cases to other subdomains, has recently been introduced. This method, called mirroring, in addition to alleviating computational costs, has some properties which fit nicely with the insights into RRT, offering solutions to some possible shortcomings of RRT. In this paper we discuss the RRT method and additional insights; we explain mirroring; and we detail applications of mirroring to RRT. The mirror RRT method proves to be a very attractive variation of RRT