The relevance of testing of memory devices of modern computing systems is shown. The methods and algorithms for implementing test procedures based on classical March tests are analyzed. Multiple March tests are highlighted to detect complex pattern-sensitive memory faults. To detect them, the necessary condition that test procedures must satisfy to deal complex faults, is substantiated. This condition is in the formation of a pseudo-exhaustive test for a given number of arbitrary memory cells. We study the effectiveness of single and double application of tests like MATS ++, March C– and March A, and also give its analytical estimates for a different number of k ≤ 10 memory cells participating in a malfunction. The applicability of the mathematical model of the combinatorial problem of the coupon collector for describing multiple memory testing is substantiated. The values of the average, minimum, and maximum multiplicity of multiple tests are presented to provide an exhaustive set of binary combinations for a given number of arbitrary memory cells. The validity of analytical estimates is experimentally shown and the high efficiency of the formation of a pseudo-exhaustive coverage by tests of the March A type is confirmed.

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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

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