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Tarasov, V., Tan, H. & Adlemo, A. (2019). Automation of software testing process using ontologies. In: J. Dietz, D. Aveiro & J. Filipe (Ed.), Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 2: KEOD: . Paper presented at 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, September 17-19, 2019, Vienna, Austria (pp. 57-66). SciTePress
Open this publication in new window or tab >>Automation of software testing process using ontologies
2019 (English)In: Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 2: KEOD / [ed] J. Dietz, D. Aveiro & J. Filipe, SciTePress, 2019, p. 57-66Conference paper, Published paper (Refereed)
Abstract [en]

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.

Place, publisher, year, edition, pages
SciTePress, 2019
Keywords
Knowledge Representation, Ontologies, Inference Rules, Test Case Generation, Automated Testing
National Category
Computer Sciences Software Engineering
Identifiers
urn:nbn:se:hj:diva-46589 (URN)10.5220/0008054000570066 (DOI)2-s2.0-85074140225 (Scopus ID)978-989-758-382-7 (ISBN)
Conference
11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, September 17-19, 2019, Vienna, Austria
Available from: 2019-10-17 Created: 2019-10-17 Last updated: 2019-11-19Bibliographically approved
Tarasov, V., Tan, H., Jarfors, A. E. .. & Seifeddine, S. (2019). Fuzzy logic-based modelling of yield strength of as-cast A356 alloy. Neural computing & applications (Print)
Open this publication in new window or tab >>Fuzzy logic-based modelling of yield strength of as-cast A356 alloy
2019 (English)In: Neural computing & applications (Print), ISSN 0941-0643, E-ISSN 1433-3058Article in journal (Refereed) Epub ahead of print
Abstract [en]

Uncertain and imprecise data are inherent to many domains, e.g. casting lightweight components. Fuzzy logic offers a way to handle such data, which makes it possible to create predictive models even with small and imprecise data sets. Modelling of cast components under fatigue load leads to understanding of material behaviour on component level. Such understanding is important for the design for minimum warranty risk and maximum weight reduction of lightweight cast components. This paper contributes with a fuzzy logic-based approach to model fatigue-related mechanical properties of as-cast components, which has not been fully addressed by the current research. Two fuzzy logic models are constructed to map yield strength to the chemical composition and the rate of solidification of castings for two A356 alloys. Artificial neural networks are created for the same data sets and then compared to the fuzzy logic approach. The comparison shows that although the neural networks yield similar prediction accuracy, they are less suitable for the domain because they are opaque models. The prediction errors exhibited by the fuzzy logic models are 3.53% for the model and 3.19% for the second, which is the same error level as reported in related work. An examination of prediction errors indicated that these are affected by parameters of the membership functions of the fuzzy logic model.

Place, publisher, year, edition, pages
Springer, 2019
Keywords
Fuzzy logic; Membership functions; Artificial neural networks; Prediction accuracy; Mechanical properties prediction; A356 alloy; Cast components
National Category
Materials Engineering Computer Engineering
Identifiers
urn:nbn:se:hj:diva-42912 (URN)10.1007/s00521-019-04056-5 (DOI)XYZ ()
Funder
Knowledge Foundation, 20170066
Available from: 2019-02-08 Created: 2019-02-08 Last updated: 2019-08-21
Tarasov, V., Seigerroth, U. & Sandkuhl, K. (2019). Ontology development strategies in industrial contexts. In: Abramowicz W., Paschke A. (Ed.), Business Information Systems Workshops. BIS 2018.: . Paper presented at 21st International Conference on Business Information Systems, BIS 2018, Berlin, Germany, in July 2018 (pp. 156-167). Cham: Springer, 339
Open this publication in new window or tab >>Ontology development strategies in industrial contexts
2019 (English)In: Business Information Systems Workshops. BIS 2018. / [ed] Abramowicz W., Paschke A., Cham: Springer, 2019, Vol. 339, p. 156-167Conference paper, Published paper (Refereed)
Abstract [en]

Knowledge-based systems are used extensively to support functioning of enterprises. Such systems need to reflect the aligned business-IT view and create shared understanding of the domain. Ontologies are used as part of many knowledge-bases systems. The industrial context affects the process of ontology engineering in terms of business requirements and technical constraints. This paper presents a study of four industrial cases that included ontology development. The study resulted in identification of seven factors that were used to compare the industrial cases. The most influential factors were found to be reuse of ontologies/models, stakeholder groups involved, and level of applicability of ontology. Finally, four recommendation were formulated for projects intended to create shared understanding in an enterprise.

Place, publisher, year, edition, pages
Cham: Springer, 2019
Series
Lecture Notes in Business Information Processing, ISSN 1865-1348
Keywords
Business and it alignment, Industrial development context, Knowledge management, Ontology engineering, Information use, Knowledge based systems, Business and it alignments, Business requirement, Industrial development, Influential factors, Ontology development, Shared understanding, Technical constraints, Ontology
National Category
Information Systems
Identifiers
urn:nbn:se:hj:diva-43205 (URN)10.1007/978-3-030-04849-5_14 (DOI)2-s2.0-85061575353 (Scopus ID)9783030048488 (ISBN)
Conference
21st International Conference on Business Information Systems, BIS 2018, Berlin, Germany, in July 2018
Available from: 2019-02-27 Created: 2019-02-27 Last updated: 2019-08-21Bibliographically approved
Adlemo, A., Hilletofth, P. & Tarasov, V. (2018). Fuzzy logic based decision-support for reshoring decisions. In: Proceedings of the 8th International Conference on Operations and Supply Chain Management: . Paper presented at International Conference on Operations and Supply Chain Management (OSCM), 9 – 12 September, 2018, Cranfield, UK.
Open this publication in new window or tab >>Fuzzy logic based decision-support for reshoring decisions
2018 (English)In: Proceedings of the 8th International Conference on Operations and Supply Chain Management, 2018Conference paper, Published paper (Refereed)
National Category
Business Administration
Identifiers
urn:nbn:se:hj:diva-41436 (URN)
Conference
International Conference on Operations and Supply Chain Management (OSCM), 9 – 12 September, 2018, Cranfield, UK
Available from: 2018-09-14 Created: 2018-09-14 Last updated: 2019-08-21Bibliographically approved
Adlemo, A., Tarasov, V., Hilletofth, P. & Eriksson, D. (2018). Knowledge intensive decision support for reshoring decisions. In: Proceedings of the 30th Annual NOFOMA Conference: Relevant Logistics and Supply Chain Management Research. Paper presented at Proceedings of the 30th NOFOMA Conference, Kolding, Denmark, 13-15 June, 2018. Kolding
Open this publication in new window or tab >>Knowledge intensive decision support for reshoring decisions
2018 (English)In: Proceedings of the 30th Annual NOFOMA Conference: Relevant Logistics and Supply Chain Management Research, Kolding, 2018Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
Kolding: , 2018
National Category
Business Administration
Identifiers
urn:nbn:se:hj:diva-41018 (URN)978-87-91070-93-8 (ISBN)
Conference
Proceedings of the 30th NOFOMA Conference, Kolding, Denmark, 13-15 June, 2018
Available from: 2018-07-19 Created: 2018-07-19 Last updated: 2019-08-21Bibliographically approved
Adlemo, A., Tarasov, V., Hilletofth, P. & Eriksson, D. (2018). Reshoring decision support in a Swedish context. In: : . Paper presented at 25th International Annual EurOMA Conference, June 24-26th, 2018 Budapest, Hungary.
Open this publication in new window or tab >>Reshoring decision support in a Swedish context
2018 (English)Conference paper, Published paper (Refereed)
Abstract [en]

This paper presents a decision-support system for reshoring decision-making based on fuzzy logic. The construction and functionality of the decision-support system are described, and the functionality is evaluated in a high cost environment exemplified through a Swedish context. Ten different reshoring scenarios, provided by Swedish reshoring experts, are entered into the decision-support system and the decision recommendations provided by the system are presented. The confidence that can be put on the recommendations is demonstrated by comparing them with those of the reshoring experts. The positive results obtained indicate that fuzzy logic is both feasible and that the quality of the results are sufficiently good for reshoring decision-making.

Keywords
Decision-Support Systems, Fuzzy Logic Systems, Reshoring
National Category
Business Administration
Identifiers
urn:nbn:se:hj:diva-41036 (URN)
Conference
25th International Annual EurOMA Conference, June 24-26th, 2018 Budapest, Hungary
Available from: 2018-07-23 Created: 2018-07-23 Last updated: 2019-08-21Bibliographically approved
Adlemo, A., Tan, H. & Tarasov, V. (2018). Test case quality as perceived in Sweden. In: Michael Unterkalmsteiner (Ed.), Proceedings - International Conference on Software Engineering: . Paper presented at 2018 ACM/IEEE 5th International Workshop on Requirements Engineering and Testing, Gothenburg, Sweden, June 02, 2018 (pp. 9-12). ACM Digital Library
Open this publication in new window or tab >>Test case quality as perceived in Sweden
2018 (English)In: Proceedings - International Conference on Software Engineering / [ed] Michael Unterkalmsteiner, ACM Digital Library, 2018, p. 9-12Conference paper, Published paper (Refereed)
Abstract [en]

In order to reach an acceptable level of confidence in the quality of a software product, testing of the software is paramount. To obtain "good" quality software it is essential to rely on "good" test cases. To define the criteria for what make up for a "good" test case is not a trivial task. Over the past 15 years, a short list of publications have presented criteria for "good" test cases but without ranking them based on their importance. This paper presents a non-exhaustive and non-authoritative tentative list of 15 criteria and a ranking of their relative importance. A number of the criteria come from previous publications but also from discussions with our industrial partners. The ranking is based on results collected via a questionnaire that was sent out to a limited number of randomly chosen respondents in the Swedish software industry. This means that the results are more indicative than conclusive.

Place, publisher, year, edition, pages
ACM Digital Library, 2018
Series
Proceedings - International Conference on Software Engineering, ISSN 0270-5257
Keywords
good software test cases, quality criteria, requirement-driven development, test-driven development
National Category
Computer Systems
Identifiers
urn:nbn:se:hj:diva-41033 (URN)10.1145/3195538.3195541 (DOI)2-s2.0-85051230713 (Scopus ID)978-1-4503-5749-4 (ISBN)
Conference
2018 ACM/IEEE 5th International Workshop on Requirements Engineering and Testing, Gothenburg, Sweden, June 02, 2018
Projects
Ontology-based software test case generation
Funder
Knowledge Foundation, 20140170
Available from: 2018-07-22 Created: 2018-07-22 Last updated: 2019-08-21Bibliographically approved
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
Tan, H., Adlemo, A., Tarasov, V. & Johansson, M. E. (2017). Evaluation of an Application Ontology. In: Stefano Borgo, Oliver Kutz, Frank Loebe, Fabian Neuhaus, Kemo Adrian, Mihailo Antović, Valerio Basile, Martin Boeker, Diego Calvanese, Tommaso Caselli, Giorgio Colombo, Roberto Confalonieri, Laura Daniele, Jérôme Euzenat, Antony Galton, Dagmar Gromann, Maria M. Hedblom, Heinrich Herre, Inge Hinterwaldner, Andrea Janes, Ludger Jansen, Kris Krois, Antonio Lieto, Claudio Masolo, Rafael Peñaloza, Daniele Porello, Daniele P. Radicioni, Emilio M. Sanfilippo, Daniel Schober, Rossella Stufano, Amanda Vizedom (Ed.), The Joint Ontology Workshops: . Paper presented at Proceedings of the Joint Ontology Workshops 2017 Episode 3: The Tyrolean Autumn of Ontology Bozen-Bolzano, Italy, September 21–23, 2017. CEUR-WS, 2050
Open this publication in new window or tab >>Evaluation of an Application Ontology
2017 (English)In: The Joint Ontology Workshops / [ed] Stefano Borgo, Oliver Kutz, Frank Loebe, Fabian Neuhaus, Kemo Adrian, Mihailo Antović, Valerio Basile, Martin Boeker, Diego Calvanese, Tommaso Caselli, Giorgio Colombo, Roberto Confalonieri, Laura Daniele, Jérôme Euzenat, Antony Galton, Dagmar Gromann, Maria M. Hedblom, Heinrich Herre, Inge Hinterwaldner, Andrea Janes, Ludger Jansen, Kris Krois, Antonio Lieto, Claudio Masolo, Rafael Peñaloza, Daniele Porello, Daniele P. Radicioni, Emilio M. Sanfilippo, Daniel Schober, Rossella Stufano, Amanda Vizedom, CEUR-WS , 2017, Vol. 2050Conference paper, Published paper (Refereed)
Abstract [en]

The work presented in this paper demonstrates an evaluation procedure for a real-life application ontology, coming from the avionics domain. The focus of the evaluation has specifically been on three ontology quality features, namely usability, correctness and applicability. In the paper, the properties of the three features are explained in the context of the application domain, the methods and tools used for the evaluation of the features are presented, and the evaluation results are presented and discussed. The results indicate that the three quality features are significant in the evaluation of our application ontology, that the proposed methods and tools allow for the evaluation of the three quality features and that the inherent quality of the application ontology can be confirmed.

Place, publisher, year, edition, pages
CEUR-WS, 2017
Series
CEUR Workshop Proceedings, ISSN 1613-0073 ; 2050
Keywords
application ontologies, ontology evaluation, ontology quality features, ontology verbalization
National Category
Computer Systems
Identifiers
urn:nbn:se:hj:diva-38902 (URN)2-s2.0-85045576954 (Scopus ID)
Conference
Proceedings of the Joint Ontology Workshops 2017 Episode 3: The Tyrolean Autumn of Ontology Bozen-Bolzano, Italy, September 21–23, 2017
Available from: 2018-02-23 Created: 2018-02-23 Last updated: 2019-08-21Bibliographically 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
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0001-6671-6157

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