Authors:
Conor Ryan
1
;
Meghana Kshirsagar
1
;
Krishn Kumar Gupt
2
;
Lukas Rosenbauer
3
and
Joseph P. Sullivan
2
Affiliations:
1
Biocomputing and Development System Lab, University of Limerick, Ireland
;
2
Department of Electrical & Electronic Engineering, Limerick Institute of Technology, Ireland
;
3
BSH Home Appliances, Im Gewerbepark B10, Regensburg, Germany
Keyword(s):
Combinational Circuit, Functional Testing, Hamming Distance, Hierarchical Clustering, Machine Learning, Test Case, Unsupervised Learning.
Abstract:
The quality assurance of circuits is of major importance as the complexity of circuits is rising with their capabilities. Thus a high degree of testing is required to guarantee proper operation. If, on the other hand, too much time is spent in testing then this prolongs development time. The work presented in this paper proposes a methodology to select a minimal set of test cases for validating digital circuits with respect to their functional specification. We do this by employing hierarchical clustering algorithms to group test cases using a hamming distance similarity measure. The test cases are selected from the clusters, by our proposed approach of distance-based selection. Results are tested on the two circuits viz. Multiplier and Galois Field multiplier that exhibit similar behaviour but differ in the number of test cases and their implementation. It is shown that on small fraction values, distance-based selection can outperform traditional random-based selection by preserving
diversity among the chosen test cases.
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