Using GQM Hypothesis Restriction to Infer Bayesian Network Testing

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 reg computer software.