Iterative Antirandom Testing

TL;DR: An iterative application of the short antirandom tests where the first test vector in each iteration is generated randomly, and a new metric the Maximal Minimal Hamming Distance (MMHD) is proposed which allows us to define an optimal antIRandom test with restricted number of patterns.

Abstract: Antirandom testing is a variation of pure random testing, which is the process of generating random patterns and applying it to a system under test (both software systems and hardware systems) However, research studies have shown that pure random testing is relatively less effective at fault detection than other testing techniques Antirandom testing improves the fault-detection capability of random testing by employing the location information of previously executed test cases In antirandom testing we select test case such that it is as different as possible from all the previous executed test cases Unfortunately, this method essentially requires enumeration of the input space and computation of each input pattern when used on an arbitrary set of existing test data This avoids scale-up to large test sets and (or) long input vectors The objective of this paper is to find a more efficient method of the test generation which does not need any computation The key idea of proposed approach is an iterative application of the short antirandom tests where the first test vector in each iteration is generated randomly Moreover, we propose a new metric the Maximal Minimal Hamming Distance (MMHD) which allows us to define an optimal antirandom test with restricted number of patterns Experimental results are given to evaluate the performance of the new approach