Implementing model redundancy in predictive alternate test to improve test confidence
| Autor: | Haithem Ayari, Florence Azais, Serge Bernard, Mariane Comte, Vincent Kcrzerho, Olivier Potin, Michel Renovell |
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| Přispěvatelé: | Conception et Test de Systèmes MICroélectroniques (SysMIC), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM) |
| Jazyk: | angličtina |
| Rok vydání: | 2013 |
| Předmět: |
Test strategy
Training set Exploit business.industry Computer science Regression analysis 02 engineering and technology Machine learning computer.software_genre 020202 computer hardware & architecture Test (assessment) 0202 electrical engineering electronic engineering information engineering Redundancy (engineering) Key (cryptography) Artificial intelligence [SPI.NANO]Engineering Sciences [physics]/Micro and nanotechnologies/Microelectronics business Implementation computer |
| Zdroj: | ETS: European Test Symposium ETS: European Test Symposium, May 2013, Avignon, France. 18th IEEE European Test Symposium, 2013, ⟨10.1109/ETS.2013.6569386⟩ ETS |
| DOI: | 10.1109/ETS.2013.6569386⟩ |
| Popis: | International audience; This work investigates new implementations of the predictive alternate test strategy that exploit model redundancy in order to improve test confidence. The key idea is to build during the training phase, not only one regression model for each specification as in the classical implementation, but several regression models. We explore various options for implementing model redundancy, based on the use of different indirect measurement combinations and/or different partitions of the training set. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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