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Publications (5 of 5) Show all publications
Tarasov, V., Tan, H., Ismail, M., Adlemo, A. & Johansson, M. (2017). Application of inference rules to a software requirements ontology to generate software test cases. In: Dragoni, Mauro; Poveda-Villalón, María; Jimenez-Ruiz, Ernesto (Ed.), OWL: Experiences and Directions – Reasoner Evaluation: 13th International Workshop, OWLED 2016, and 5th International Workshop, ORE 2016, Bologna, Italy, November 20, 2016, Revised Selected Papers (pp. 82-94). Cham: Springer
Open this publication in new window or tab >>Application of inference rules to a software requirements ontology to generate software test cases
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2017 (English)In: OWL: Experiences and Directions – Reasoner Evaluation: 13th International Workshop, OWLED 2016, and 5th International Workshop, ORE 2016, Bologna, Italy, November 20, 2016, Revised Selected Papers / [ed] Dragoni, Mauro; Poveda-Villalón, María; Jimenez-Ruiz, Ernesto, Cham: Springer, 2017, p. 82-94Chapter in book (Refereed)
Abstract [en]

Testing of a software system is resource-consuming activity. One of the promising ways to improve the efficiency of the software testing process is to use ontologies for testing. This paper presents an approach to test case generation based on the use of an ontology and inference rules. The ontology represents requirements from a software requirements specification, and additional knowledge about components of the software system under development. The inference rules describe strategies for deriving test cases from the ontology. The inference rules are constructed based on the examination of the existing test documentation and acquisition of knowledge from experienced software testers. The inference rules are implemented in Prolog and applied to the ontology that is translated from OWL functional-style syntax to Prolog syntax. The first experiments with the implementation showed that it was possible to generate test cases with the same level of detail as the existing, manually produced, test cases.

Place, publisher, year, edition, pages
Cham: Springer, 2017
Series
Lecture Notes in Computer Science, ISSN 0302-9743 ; 10161
Keywords
Inference Rules, Ontology, OWL, Prolog, Requirement Specification, Test Case Generation
National Category
Computer Sciences Software Engineering
Identifiers
urn:nbn:se:hj:diva-35164 (URN)10.1007/978-3-319-54627-8_7 (DOI)000426195800007 ()2-s2.0-85014455857 (Scopus ID)978-3-319-54627-8 (ISBN)
Projects
Ontology-based Software Test Case Generation (OSTAG)
Funder
Knowledge Foundation, 20140170
Available from: 2017-03-07 Created: 2017-03-07 Last updated: 2019-08-22Bibliographically approved
Ismail, M. (2017). Ontology Learning from Software Requirements Specification. In: Dragoni, Mauro; Poveda-Villalón, María; Jimenez-Ruiz, Ernesto (Ed.), Paolo Ciancarini, Francesco Poggi, Matthew Horridge, Jun Zhao, Tudor Groza, Mari Carmen Suarez-Figueroa, Mathieu d'Aquin, Valentina Presutti (Ed.), : . Paper presented at EKAW 2016 Satellite Events, EKM and Drift-an-LOD, Bologna, November 19–23, 2016. (pp. 251-255). Springer
Open this publication in new window or tab >>Ontology Learning from Software Requirements Specification
2017 (English)In: / [ed] Paolo Ciancarini, Francesco Poggi, Matthew Horridge, Jun Zhao, Tudor Groza, Mari Carmen Suarez-Figueroa, Mathieu d'Aquin, Valentina Presutti, Springer, 2017, p. 251-255Conference paper, Published paper (Refereed)
Abstract [en]

Learning ontologies from software requirements specifications with individuals and relations between individuals to represent detailed information, such as input, condition and expected result of a requirement, is a difficult task. System specification ontologies (SSOs) can be developed from software requirement specifications to represent requirements and can be used to automate some time-consuming activities in software development processes. However, manually developing SSOs to represent requirements and domain knowledge of a software system is a time-consuming and a challenging task. The focus of this PhD is how to create ontologies semi-automatically from SRS. We will develop a framework that can be a possible solution to create semi-automatically ontologies from SRS. The developed framework will mainly be evaluated by using the constructed ontologies in the software testing process and automating a part of it. i.e. test case generation.

Place, publisher, year, edition, pages
Springer, 2017
Series
Lecture Notes in Computer Science, ISSN 0302-9743 ; 10180
Keywords
Ontology, Ontology learning, Software requirements specification, Software development process
National Category
Computer Sciences
Identifiers
urn:nbn:se:hj:diva-35618 (URN)10.1007/978-3-319-58694-6_39 (DOI)000428943000041 ()2-s2.0-85019767479 (Scopus ID)978-3-319-58694-6 (ISBN)978-3-319-58693-9 (ISBN)
Conference
EKAW 2016 Satellite Events, EKM and Drift-an-LOD, Bologna, November 19–23, 2016.
Available from: 2017-05-30 Created: 2017-05-30 Last updated: 2018-07-19Bibliographically approved
Ismail, M. (2017). Ontology learning from software requirements specifications: Research Proposal.
Open this publication in new window or tab >>Ontology learning from software requirements specifications: Research Proposal
2017 (English)Report (Other academic)
Abstract [en]

Using ontologies is a promising way to automate some time-consuming activitiesin software development processes, such as requirements analysis and validation,and software verification. Ontologies can also be used to support software developmentprocesses by sharing domain knowledge between development phases. Systemspecification ontologies (SSOs) can be developed from software requirementsspecifications (SRS) to represent requirements and knowledge about the softwaresystem that is being developed. SSOs can be interpreted by a tool to infer the knowledgethat can be used to automate some time-consuming activities in software developmentprocesses, in order to reduce the cost and time of the software development.However, manually developing SSOs to represent the software requirementsknowledge is a time-consuming and a challenging task.

The focus of this PhD project is to understand how to generate system specificationontologies semi-automatically from software requirements specifications. This thesiswill guide how software requirements specifications can be processed to capturedomain knowledge and knowledge about the software system, and represent softwarerequirements in an ontology semi-automatically. We will develop a frameworkand a set of methods within this framework which can be a possible solution tocreate semi-automatically ontologies from SRS. The constructed ontologies can beused in software processes to automate time-consuming activities, such as requirementsvalidation and software test case generations. The developed framework andmethods will be evaluated by comparing with existing ontology learning methodsand by using constructed ontologies to automate test case generation which is partof software testing process. The proposed solution will be evaluated by in differentcase studies that can have similar software requirements specifications.

Publisher
p. 28
Keywords
Ontology, Ontology Learning, Software Requirements Specifications (SRS), Software Testing
National Category
Computer Sciences
Identifiers
urn:nbn:se:hj:diva-35432 (URN)
Note

Research proposal, PhD programme, University of Skövde

Available from: 2017-04-28 Created: 2017-04-28 Last updated: 2018-01-13Bibliographically approved
Tan, H., Ismail, M., Tarasov, V., Adlemo, A. & Johansson, M. (2016). Development and evaluation of a software requirements ontology. In: Iaakov Exman, Juan Llorens and Anabel Fraga (Ed.), SKY 2016 - 7th International Workshop on Software Knowledge, Proceedings - In conjuction with IC3K 2016: . Paper presented at 7th International Workshop on Software Knowledge - SKY 2016 in conjunction with the 9th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - IC3K 2016, November 9-10, 2016, in Porto, Portugal (pp. 11-18). SciTePress
Open this publication in new window or tab >>Development and evaluation of a software requirements ontology
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2016 (English)In: SKY 2016 - 7th International Workshop on Software Knowledge, Proceedings - In conjuction with IC3K 2016 / [ed] Iaakov Exman, Juan Llorens and Anabel Fraga, SciTePress, 2016, p. 11-18Conference paper, Published paper (Refereed)
Abstract [en]

This paper presents an ontology which has been developed to represent the requirements of a software component pertaining to an embedded system in the avionics industry. The ontology was built based on the software requirements documents and was used to support advanced methods in the subsequent stages of the software development process. In this paper it is described theprocess that was used to build the ontology. Two pertinent quality measures that were applied to the ontology, i.e. usability and applicability, are also described, as well as the methods used to evaluate the quality measures and the result of these evaluations.

Place, publisher, year, edition, pages
SciTePress, 2016
Keywords
Ontology, Software Requirements, Ontology Development, Ontology Evaluation, Avionics Software Development
National Category
Computer Systems
Identifiers
urn:nbn:se:hj:diva-32277 (URN)10.5220/0006079300110018 (DOI)000393154100001 ()2-s2.0-85007006434 (Scopus ID)978-989-758-202-8 (ISBN)
Conference
7th International Workshop on Software Knowledge - SKY 2016 in conjunction with the 9th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - IC3K 2016, November 9-10, 2016, in Porto, Portugal
Projects
Ontology-based Software Test Case Generation (OSTAG)
Funder
Knowledge Foundation, KKS-20140170
Available from: 2016-11-16 Created: 2016-11-16 Last updated: 2019-08-21Bibliographically approved
Tarasov, V., Tan, H., Adlemo, A., Ismail, M., Mats, J. & Olsson, D. (2015). Ontology-based Software Test Case Generation (OSTAG). In: R. J. Machado, J. Sequeira,H. Plácido da Silva, & J. Filipe (Ed.), European Projects in Knowledge Applications and Intelligent Systems - Volume 1: EPS Lisbon 2016: . Paper presented at European Projects in Knowledge Applications and Intelligent Systems, July 20-23, 2016, Lisbon, Portugal (pp. 135-159). SciTePress
Open this publication in new window or tab >>Ontology-based Software Test Case Generation (OSTAG)
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2015 (English)In: European Projects in Knowledge Applications and Intelligent Systems - Volume 1: EPS Lisbon 2016 / [ed] R. J. Machado, J. Sequeira,H. Plácido da Silva, & J. Filipe, SciTePress, 2015, p. 135-159Conference paper, Published paper (Refereed)
Abstract [en]

Testing is a paramount quality assurance activity in every software developmentproject, especially for embedded, safety critical systems. During thetest process, a lot of effort is put into the generation of test cases. The presentedOSTAG project aimed at developing methods and techniques to automate thesoftware test case generation for black-box testing. The proposed approach wasbased on the creation of a software requirements ontology and the applicationof inference rules on the ontology to derive test cases. The ontology representsknowledge of the requirements, the software system and the corresponding applicationdomain while the inference rules formalize knowledge from documentsand experienced testers in the domain of test planning and test case generation.A software prototype of the approach was implemented and one of the industrialproject partners evaluated the results. An alternative method for generating testcases, based on genetic algorithms, was also explored.

Place, publisher, year, edition, pages
SciTePress, 2015
Keywords
Black-box Testing, Embedded Systems, Genetic Algorithms, Inference Rules, Knowledge Modelling, Model-Based Testing, Ontology Development, Ontology Quality Evaluation, Ontology Verbalisation, OWL, Prolog, Prot´eg´e, Software Requirements Specification, Test Case Generation.
National Category
Computer Systems
Identifiers
urn:nbn:se:hj:diva-43426 (URN)10.5220/0007901301350159 (DOI)978-989-758-356-8 (ISBN)
Conference
European Projects in Knowledge Applications and Intelligent Systems, July 20-23, 2016, Lisbon, Portugal
Funder
Knowledge Foundation, 20140170
Available from: 2019-04-04 Created: 2019-04-04 Last updated: 2019-08-21Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0003-3113-7416

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