An Ontology-based Knowledge Acquisition Model for Software Anomalies Systems

Vast knowledge abounds in the field of anomalies in software systems, especially when these anomalies are rightly and wholly classified. However, it has been hard to find this knowledge in a single repository to yield potentially useful insights into understanding the types of errors inherent in a system to correct them to yield robust software systems ultimately. The few earlier studies geared towards this direction could not adequately address this issue due to the inadequacy of the techniques employed. This paper aims to correct this phenomenon by using the Protégé knowledge acquisition tool to build a prototype ontology-based knowledge system. Ontologies— consensual machine-readable resources—are designed to promote the sharing, interoperability, and reuse of knowledge. The ontology built is integrated with the inference rule of an expert system to produce the knowledge system where both elicitation and validation of domain facts are done. This will, therefore, lead to the effortless building of seamless, robust, and integrity-filled systems whose knowledge is reusable and sharable.