On the evaluation of arbitrary defect coverage of test sets

2003 ◽  
Author(s):  
A. Jain ◽  
V. Boppana ◽  
M.S. Hsiao ◽  
M. Fujita
Keyword(s):  
Defect Coverage ◽  
Test Sets

Compact test sets for high defect coverage

1997 ◽  
Vol 16 (8) ◽  
pp. 923-930 ◽  
Author(s):  
S.M. Reddy ◽  
I. Pomeranz ◽  
S. Kajihara
Keyword(s):  
Defect Coverage ◽  
Test Sets ◽  
Compact Test

Linguistische Fairness und differentielle Validität des Auditiven Wortlisten Lerntests (AWLT) bei Demenz vom Alzheimer-Typ

Diagnostica ◽  
2019 ◽  
Vol 65 (4) ◽  
pp. 193-204
Author(s):  
Johannes Baltasar Hessler ◽  
David Brieber ◽  
Johanna Egle ◽  
Georg Mandler ◽  
Thomas Jahn
Keyword(s):  
Cognitive Functions ◽  
Kognitive Funktionen ◽  
Alzheimer Demenz ◽  
Test Sets

Zusammenfassung. Der Auditive Wortlisten Lerntest (AWLT) ist Teil des Test-Sets Kognitive Funktionen Demenz (CFD; Cognitive Functions Dementia) im Rahmen des Wiener Testsystems (WTS). Der AWLT wurde entlang neurolinguistischer Kriterien entwickelt, um Interaktionen zwischen dem kognitiven Status der Testpersonen und den linguistischen Eigenschaften der Lernliste zu reduzieren. Anhand einer nach Alter, Bildung und Geschlecht parallelisierten Stichprobe von gesunden Probandinnen und Probanden ( N = 44) und Patientinnen und Patienten mit Alzheimer Demenz ( N = 44) wurde mit ANOVAs für Messwiederholungen überprüft, inwieweit dieses Konstruktionsziel erreicht wurde. Weiter wurde die Fähigkeit der Hauptvariablen des AWLT untersucht, zwischen diesen Gruppen zu unterscheiden. Es traten Interaktionen mit geringer Effektstärke zwischen linguistischen Eigenschaften und der Diagnose auf. Die Hauptvariablen trennten mit großen Effektstärken Patientinnen und Patienten von Gesunden. Der AWLT scheint bei vergleichbarer differenzieller Validität linguistisch fairer als ähnliche Instrumente zu sein.

Addressing Defect Coverage through Generating Test Vectors for Transistor Defects

2009 ◽  
Vol E92-A (12) ◽  
pp. 3128-3135
Author(s):  
Yoshinobu HIGAMI ◽  
Kewal K. SALUJA ◽  
Hiroshi TAKAHASHI ◽  
Shin-ya KOBAYASHI ◽  
Yuzo TAKAMATSU
Keyword(s):  
Defect Coverage ◽  
Test Vectors

QSAR Studies of Halogenated Pyrimidine Derivatives as Inhibitors of Human Dihydroorotate Dehydrogenase Using Modified Bee Algorithm

2018 ◽  
Vol 21 (5) ◽  
pp. 381-387 ◽  
Author(s):  
Hossein Atabati ◽  
Kobra Zarei ◽  
Hamid Reza Zare-Mehrjardi
Keyword(s):  
External Validation ◽  
Correlation Coefficients ◽  
Quantitative Structure Activity Relationship ◽  
Molecular Structures ◽  
Dihydroorotate Dehydrogenase ◽  
Pyrimidine Derivatives ◽  
Qsar Studies ◽  
Bee Algorithm ◽  
Test Sets ◽  
Leave One Out

Aim and Objective: Human dihydroorotate dehydrogenase (DHODH) catalyzes the fourth stage of the biosynthesis of pyrimidines in cells. Hence it is important to identify suitable inhibitors of DHODH to prevent virus replication. In this study, a quantitative structure-activity relationship was performed to predict the activity of one group of newly synthesized halogenated pyrimidine derivatives as inhibitors of DHODH. Materials and Methods: Molecular structures of halogenated pyrimidine derivatives were drawn in the HyperChem and then molecular descriptors were calculated by DRAGON software. Finally, the most effective descriptors for 32 halogenated pyrimidine derivatives were selected using bee algorithm. Results: The selected descriptors using bee algorithm were applied for modeling. The mean relative error and correlation coefficient were obtained as 2.86% and 0.9627, respectively, while these amounts for the leave one out−cross validation method were calculated as 4.18% and 0.9297, respectively. The external validation was also conducted using two training and test sets. The correlation coefficients for the training and test sets were obtained as 0.9596 and 0.9185, respectively. Conclusion: The results of modeling of present work showed that bee algorithm has good performance for variable selection in QSAR studies and its results were better than the constructed model with the selected descriptors using the genetic algorithm method.

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