This paper is joint work with Dimitrios Settas and Antonio Cerone from United Nations University (Macau). It will be presented at the 9th ACIS International Conference on Software Engineering Research, Management and Applications (SERA 2011) in Baltimore, Maryland, USA. You can download the full paper from the Publications section in September 2011.
Abstract: 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.