Title
Using GQM Hypothesis Restriction to Infer Bayesian Network Testing
Abstract
By definition, the scope of a Bayesian Network uses a complementary technique to restrict the modeling reach. In this paper, the used restriction technique was the Goals, Questions, and Metrics (GQM). The hypothesis to be tested relates cause and effect conditional probabilities in a software test phase of a manufacturing production line. The Bayesian Network concept is related to the specific concept of a Directed Non Cyclic Graph (DNCG), where each one of its nodes represents a random discrete variable and is illustrated by directed arcs of cause and effect relationships between variables. A Bayesian Network is a graphical artifact which restricts problems, incorporating data structures. The major contributions of this paper are conceptualization and implementation of a methodology for using a GQM hypothesis restriction to infer Bayesian network testing with the Netica Bayesian Networks ® computer software.
Year
DOI
Venue
2009
10.1109/ITNG.2009.303
ITNG
Keywords
Field
DocType
software test phase,bayesian network testing,computer software,bayesian network,netica bayesian networks,effect conditional probability,gqm hypothesis restriction,effect relationship,infer bayesian network testing,complementary technique,bayesian network concept,manufacturing,data structure,bayesian methods,production,databases,test,data mining,gqm,software metrics,computer networks,application software,information technology,probabilistic logic,expert systems,testing,software testing,conditional probability,directed graphs
Data mining,Variable-order Bayesian network,GQM,Conditional probability,Computer science,Directed graph,Bayesian network,Artificial intelligence,Bayesian statistics,Software metric,Machine learning,Bayesian probability
Conference
Citations 
PageRank 
References 
4
0.78
1
Authors
6