The paper is devoted to the research and development of methods and tools for identifying problematic situations on the basis of ontologies using the mechanisms of logical inference that are used in intellectual decision support systems for software testing problems.
The important problem of software testing using ontological modeling for timely detection of errors and improvement of quality of the developed software is considered.
Using ontological modeling to represent and identify situations creates additional opportunities and constraints to solving the identification problem. The advantage is the ability to use logical inference and use axioms in the process of reasoning about situations. This opens up the prospect of developing methods for identifying situations based on logical inference based on information about the current state of the subject area and knowledge about the subject area.
The used situation models allows not only automate the execution of some simple tasks, but as well as to make logical reasoning in the testing systems based on your existing knowledge of situations.
The ontological knowledge presentation of domain made possible to formalize knowledge about the problematic situations that arise on the project. Application of the developed methods of situation identification in the system ensured timely identification of threatening situations and formation of recommendations for their elimination. All these factors help to increase the quality of the software product during its development.
The ontology of the software testing industry is presented in the paper, as well as the algorithm of the system operation and modeling based on UML.
The architecture of the situation identification system was developed, as well as the software for the analysis and modeling of problem situations on the example of decision support systems of the field of software testing. The central component of software is the ontological modeling tool — Protégé.
The plugins SWRL and SQWRL were used to extend the functionality of the Protégé, and provide necessary modeling functionality.
It is advisable to use the results of the work to solve the problems of identifying critical situations during the development and testing of software, reuse the software quality control information in knowledge bases and thus increase the quality of created software.
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