An Ontology-Based Approach to Automated Test Case Generation

Software testing is as old as software itself. However, the techniques, tools, and processes used by researchers to ensure product quality are constantly evolving. Application of knowledge management technologies in automated test case generation is one of them. This paper addressed the issue of ontology-based automated test case generation in the case of black box testing. In this context, several challenges are present in existing literature. The prime challenges among are (1) major approaches are confined to a specific domain, (2) least consideration about modified domain knowledge, (3) lack of methodology for auto-identification of pre-conditions and different combinations among test input data and (4) poor requirements and domain coverage. The proposed methodology, in this paper, is aimed to resolve these issues by devising a rule-based reasoner that can auto generate the test cases. The proposed method takes an ontology-based requirements specification as an input. The novelty of the proposed method is the specification of domain independent inference rules based on which the devised reasoner can generate test cases for different domains and systems automatically. This contribution of the proposed work facilitates in improving both user’s requirements coverage and domain coverage. The devised reasoned, in this paper, is implemented in Apache Jena (Apache Jena, https://jena.apache.org ., Accessed 2020/09/04). In addition, the usability of the proposed work is illustrated using a suitable case study.