Automated prediction of defect severity based on codifying design knowledge using ontologies
Martin Iliev, Bilal Karasneh, Michel R. V. Chaudron, Edwin Essenius · 2012
Assessing severity of software defects is essential for prioritizing fixing activities as well as for assessing whether the quality level of a software system is good enough for release. In filling out defect reports, developers routinely fill out default values for the severity levels. The purpose of this research is to automate the prediction of defect severity. Our aim is to research how this severity prediction can be achieved through reasoning about the requirements and the design of a system using ontologies. In this paper we outline our approach based on an industrial case study.