Specifying a program control path using flow diagram translated into description logic of ontology engineering
Software testing plays a vital role in improving the performance of software by detecting and fixing bugs and faults which cause software failure. However, software testing is an expensive task, labor-intensive and time-consuming process in the software development life cycle. Every software product needs to be tested in order to make sure it achieves all of its goals and to detect any unexpected behaviour. One of the most critical features in structural testing is path testing which helps to find every possible executable path that helps to determine all faults lying within a piece of code. Any software pro-gram includes multiple entries and exit points. Testing each of these points is challenging as well as time-consuming. To reduce this complexity and time consuming, the use of ontologies might prove useful. Ontology is a technique used at one or more software lifecycle phases. Ontology allows for the definition of a common vocabulary and frame-work among users (either human or machines). Software development has benefited from this conceptual modelling, allowing a common understanding of the concepts involved in the software process. This paper is proposed to improve test path generation of control ow graph. The basic idea is to use OWL-DL ontology as the knowledge representation formalism, to model and specify control ow by adding semantics to control ow graph entities and adding semantics to the dynamic behaviour of control ow relations.
Faculty of Computer Science and Artificial Intelligence
Physical Sciences, General Computer Science, General Engineering
Indexed in Scopus
Basis path, Control flow graph (CFG), Cyclomatic complexity (CC), Ontology, OWL-DL, Software testing
Ibrahim, Shimaa; Hassan, Yasser Fouad; and Kholief, Mohamed Hamed, "Specifying a program control path using flow diagram translated into description logic of ontology engineering" (2020). Faculty of Computer Science & Artificial Intelligence. 2.