Iterative Antirandom Testing

Mrozek, Ireneusz; Yarmolik, V. N.

doi:10.1007/s10836-011-5272-1

Cited by 14 publications

(12 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This subsection introduces definitions of critical terms used throughout the paper. The definitions are in accordance with the previous literature [5], [6], [19]- [21], [23]- [25], [32], [34]- [37]. All definitions are true for a test sequence…”

Section: A Definitions Of Critical Termssupporting

confidence: 59%

“…At the same time, Iterative Antirandom (IAR) amplifies the fault coverage by proposing a localized distance metric, maximum-minimum Hamming distance (MMHD). IAR suggests maximization of MMHD for a short testing sequence [34]. Following IAR, Controlled random testing generates short test sequences using predetermined lengths of q = 2, 3 and 4 test patterns [35].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Multiple Controlled Antirandom Testing (MCAT) for High Fault Coverage in a Black Box Environment

et al. 2019

View full text Add to dashboard Cite

Among the black-box approaches to digital circuit testing, Random testing is popular due to its simplicity and cost effectiveness. Unfortunately, available evidences suggest that Random testing is equipped with a number of redundant patterns that increase test length without significantly raising the fault coverage. An extension to Random testing is Antirandom that removes redundancy by introducing a divergent pattern with every subsequent test pattern selection. A divergent pattern is induced by maximizing the Hamming distance and Cartesian distance of every subsequent test pattern from the set of previously applied test patterns. However, an enumeration of input combinations is required for the selection of a divergent pattern. Therefore, selection of a divergent pattern from all input combinations restricts the scalability of an Antirandom test pattern generation. One of the recently considered approaches is the stacking of locally optimized short sequences to generate a complete test sequence. Locally optimized short sequences originate from randomly chosen patterns instead of divergent patterns to avoid enumeration of input space. Seeding of random patterns for short sequences affects global diversity of the generated test sequence and hence, fault coverage is compromised. Therefore, this paper firstly proposes a tree traversal search based selection of divergent patterns that eliminates the search space. Ease in divergent pattern selection is used to generate optimal short sequences for divergent patterns instead of random patterns. Consequently, Multiple Controlled Antirandom Tests (MCATs) are generated that maximize distance between locally optimal short sequences to elevate the fault coverage. Fault simulation results on both ISCAS'85 and ISCAS'89 benchmark circuits prove the scalability and effectiveness of the proposed approach. Moreover, the comparison shows that up to 12% of fault coverage is improved as a result of proposed MCAT test pattern generation. INDEX TERMS Antirandom, test pattern generation, computation reduction, multiple controlled antirandom tests.

show abstract

Section: A Definitions Of Critical Termssupporting

confidence: 59%

Section: Introductionmentioning

confidence: 99%

Multiple Controlled Antirandom Testing (MCAT) for High Fault Coverage in a Black Box Environment

et al. 2019

View full text Add to dashboard Cite

show abstract

“…Unfortunately the basic antirandom method essentially requires enumeration of the input space and computation of distances for each potential input vector [14]. Therefore many modification of antirandom tests have been proposed [15][16][17][18][19][20][21][22][23][24]. But even for improved version of the method, very often computations become too expensive for real dimension N of the test vectors [25].…”

Section: Introductionmentioning

confidence: 99%

Optimal Controlled Random Tests

Mrozek

Yarmolik

2017

Computer Information Systems and Industrial Management

Self Cite

View full text Add to dashboard Cite

Part 1: AlgorithmsInternational audienceControlled random tests, methods of their generation, main criteria used for their synthesis, such as the Hamming distance and the Euclidean distance, as well as their application to the testing of both hardware and software systems are discussed. Available evidences suggest that high computational complexity is one of the main drawbacks of these methods. Therefore we propose a technique to overcome this problem. In the paper we propose the algorithm for optimal controlled random tests generation. Both experimental and analytical investigation clearly show the high efficiency of proposed solution especially for the multi-run tests with small number of iterations. The given tests can be applied for hardware and software testing but it seems they may be particularly interesting from the perspective of the effective detection of pattern sensitive faults in RAMs

show abstract

“…Testing sequence is a collection of test patterns represented by T. As shown in (1) and (2) report definitions for CD and TCD. These definitions are true for testing sequence and test pattern where for an N-input circuit under test [1,2,7,8,10,11,18,20,27,28].…”

Section: Methodsmentioning

confidence: 99%

“…Consequently, a candidate with maximum of the minimum distances is selected. All these algorithms successfully reduce the computational complexity at the cost of compromised fault coverage [1,2,7,8,10,27,28].…”

Section: Introductionmentioning

confidence: 99%

Adaptive random testing with total cartesian distance for black box circuit under test

Alamgir

A’ain

Paraman

et al. 2020

IJEECS

View full text Add to dashboard Cite

<p>Testing and verification of digital circuits is of vital importance in electronics industry. Moreover, key designs require preservation of their intellectual property that might restrict access to the internal structure of circuit under test. Random testing is a classical solution to black box testing as it generates test patterns without using the structural implementation of the circuit under test. However, random testing ignores the importance of previously applied test patterns while generating subsequent test patterns. An improvement to random testing is Antirandom that diversifies every subsequent test pattern in the test sequence. Whereas, computational intensive process of distance calculation restricts its scalability for large input circuit under test. Fixed sized candidate set adaptive random testing uses predetermined number of patterns for distance calculations to avoid computational complexity. A combination of max-min distance with previously executed patterns is carried out for each test pattern candidate. However, the reduction in computational complexity reduces the effectiveness of test set in terms of fault coverage. This paper uses a total cartesian distance based approach on fixed sized candidate set to enhance diversity in test sequence. The proposed approach has a two way effect on the test pattern generation as it lowers the computational intensity along with enhancement in the fault coverage. Fault simulation results on ISCAS’85 and ISCAS’89 benchmark circuits show that fault coverage of the proposed method increases up to 20.22% compared to previous method.</p>

show abstract

Iterative Antirandom Testing

Cited by 14 publications

References 28 publications

Multiple Controlled Antirandom Testing (MCAT) for High Fault Coverage in a Black Box Environment

Multiple Controlled Antirandom Testing (MCAT) for High Fault Coverage in a Black Box Environment

Optimal Controlled Random Tests

Adaptive random testing with total cartesian distance for black box circuit under test

Contact Info

Product

Resources

About