Journal article
A cone-based genetic optimization procedure for test generation and its application to n-detections in combinational circuits
IEEE transactions on computers, Vol.48(10), pp.1145-1152
10/1999
DOI: 10.1109/12.805164
Abstract
Test generation procedures based on genetic optimization were shown to be effective in achieving high fault coverage for benchmark circuits. In this work, we propose a representation of test patterns for genetic optimization based test generation, where subsets of inputs are considered as indivisible entities. Using this representation, crossover between two test patterns t/sub 1/ and t/sub 2/ copies all the values of each subset either from t/sub 1/ or from t/sub 2/. By keeping input subsets undivided, activation and propagation capabilities of t/sub 1/ and t/sub 2/ are expected to be captured and carried over to the new test patterns. Experimental results presented show that the proposed scheme results in complete stuck-at test sets and n-detection test sets for combinational circuits, even in cases where other procedures report incomplete fault coverages.
Details
- Title: Subtitle
- A cone-based genetic optimization procedure for test generation and its application to n-detections in combinational circuits
- Creators
- I Pomeranz - University of IowaS.M Reddy - University of Iowa
- Resource Type
- Journal article
- Publication Details
- IEEE transactions on computers, Vol.48(10), pp.1145-1152
- Publisher
- IEEE
- DOI
- 10.1109/12.805164
- ISSN
- 0018-9340
- eISSN
- 1557-9956
- Language
- English
- Date published
- 10/1999
- Academic Unit
- Electrical and Computer Engineering
- Record Identifier
- 9984197348702771
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