Authors:
Vladimir Tarasov
;
He Tan
and
Anders Adlemo
Affiliation:
School of Engineering, Jönköping University, Jönköping and Sweden
Keyword(s):
Knowledge Representation, Ontologies, Inference Rules, Test Case Generation, Automated Testing.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Communication and Software Technologies and Architectures
;
Data Engineering
;
e-Business
;
Enterprise Information Systems
;
Information Systems Analysis and Specification
;
Knowledge Engineering
;
Knowledge Engineering and Ontology Development
;
Knowledge Representation
;
Knowledge-Based Systems
;
Ontologies and the Semantic Web
;
Ontology Engineering
;
Symbolic Systems
Abstract:
Testing of a software system is a resource-consuming activity that requires high-level expert knowledge. Methods based on knowledge representation and reasoning can alleviate this problem. This paper presents an approach to enhance the automation of the testing process using ontologies and inference rules. The approach takes software requirements specifications written in structured text documents as input and produces the output, i.e. test scripts. The approach makes use of ontologies to deal with the knowledge embodied in requirements specifications and to represent the desired structure of test cases, as well as makes use of a set of inference rules to represent strategies for deriving test cases. The implementation of the approach, in the context of an industrial case, proves the validity of the overall approach.