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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
Tan, H., Tarasov, V., Jarfors, A. E. .. & Seifeddine, S. (2019). Fuzzy Logic Based Modelling of Cast Component Properties. Paper presented at 9th IFAC Conference on Manufacturing Modelling, Management and Control MIM 2019: Berlin, Germany, 28–30 August 2019. IFAC-PapersOnLine, 52(13), 1132-1137
Open this publication in new window or tab >>Fuzzy Logic Based Modelling of Cast Component Properties
2019 (English)In: IFAC-PapersOnLine, E-ISSN 2405-8963, Vol. 52, no 13, p. 1132-1137Article in journal (Refereed) Published
Abstract [en]

Digitalization of manufacturing requires building models to represent accumulated data and knowledge on the products and processes. The use of formal knowledge models allows for increase of the automation level leading to more sustainable manufacturing. Casting is important for different industries because it offers a great freedom of designing for weight reduction. This paper presents an approach to modelling of cast component properties that is based on fuzzy logic. The approach includes learning of the fuzzy inference rules from the data. The constructed fuzzy logic models can be used to tune the manufacturing process to produces cast components with desired properties. The evaluation of the results demonstrates that the accuracy of the two created models are 3.58% and 3.15% respectively with the learned fuzzy inference rules being identical to the manually created ones. The presented approach can help to automate the management of cast component manufacturing. 

Place, publisher, year, edition, pages
Elsevier, 2019
Keywords
knowledge modelling; fuzzy logic; fuzzy systems; mechanical properties prediction
National Category
Other Computer and Information Science
Identifiers
urn:nbn:se:hj:diva-47362 (URN)10.1016/j.ifacol.2019.11.348 (DOI)000504282400193 ()2-s2.0-85078888003 (Scopus ID)POA JTH 2019 (Local ID)POA JTH 2019 (Archive number)POA JTH 2019 (OAI)
Conference
9th IFAC Conference on Manufacturing Modelling, Management and Control MIM 2019: Berlin, Germany, 28–30 August 2019
Available from: 2020-01-14 Created: 2020-01-14 Last updated: 2020-02-24Bibliographically 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
Tan, H., Tarasov, V. & Adlemo, A. (2019). Lessons Learned from an Application of Ontologies in Software Testing. In: Adrien Barton, Selja Seppälä, Daniele Porello, et.al. (Ed.), CEUR Workshop Proceedings: . Paper presented at JOWO 2019, The Joint Ontology Workshops, Graz, Austria, September 23-25, 2019.. CEUR-WS, 2518
Open this publication in new window or tab >>Lessons Learned from an Application of Ontologies in Software Testing
2019 (English)In: CEUR Workshop Proceedings / [ed] Adrien Barton, Selja Seppälä, Daniele Porello, et.al., CEUR-WS , 2019, Vol. 2518Conference paper, Published paper (Refereed)
Abstract [en]

Testing of a software system is a resource-consuming activity that requires high-level expert knowledge. In previous work we proposed an ontologybased approach to alleviate this problem. In this paper we discuss the lessons learned from the implementation and application of the approach in a use case from the avionic industry. The lessons are related to the areas of ontology development, ontology evaluation, the OWL language and rule-based reasoning.

Place, publisher, year, edition, pages
CEUR-WS, 2019
Series
CEUR Workshop Proceedings, E-ISSN 1613-0073
Keywords
application of ontologies, OWL, Prolog, lesson learned, test case generation, automated testing
National Category
Software Engineering
Identifiers
urn:nbn:se:hj:diva-47361 (URN)2-s2.0-85077693642 (Scopus ID)
Conference
JOWO 2019, The Joint Ontology Workshops, Graz, Austria, September 23-25, 2019.
Available from: 2020-01-13 Created: 2020-01-13 Last updated: 2020-01-29Bibliographically approved
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
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0001-6671-6157

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