Generation of Bayesian Networks using the Antipatten Ontology

Apart from the plethora of antipatterns that are inherently informal and imprecise, the information used in the antipattern ontology itself is many times imprecise or vaguely defined. For example, the certainty in which a cause, symptom or consequence of an antipattern exists in a software project. However, ontologies are not capable of representing uncertainty and the effective detection of antipatterns taking into account the uncertainty that exists in software projects, stills remain an open issue. Bayesian Networks (BNs) have been previously used in order to measure, illustrate and handle antipattern uncertainty in mathematical terms. In this paper, we explore the ways in which the antipattern ontology can be used to generate Bayesian Networks. This approach allows software developers to quantify the existence or occurrence of an antipattern attribute using Bayesian Networks, based on probabilistic knowledge contained in the antipattern ontology regarding antipatterns attributes. The approach is exemplified with an ontology-based model generated using BNTab.