Title :
Data mining in functional test content optimization
Author_Institution :
Univ. of California at Santa Barbara, Santa Barbara, CA, USA
Abstract :
This paper reviews the data mining methodologies proposed for functional test content optimization where tests are sequences of instructions or transactions. Basic machine learning concepts and the key ideas of these methodologies are explained. Challenges for implementing these methodologies in practice are illustrated. Promises are demonstrated through experimental results based on industrial verification settings.
Keywords :
data mining; learning (artificial intelligence); optimisation; data mining; functional test content optimization; industrial verification settings; machine learning; Assembly; Context; Data mining; Generators; Kernel; Machine learning algorithms; Optimization;
Conference_Titel :
Design Automation Conference (ASP-DAC), 2015 20th Asia and South Pacific
Conference_Location :
Chiba
Print_ISBN :
978-1-4799-7790-1
DOI :
10.1109/ASPDAC.2015.7059023
