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Model‐based generation of test scripts across product variants: An experience report from the railway industry
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0002-8027-0611
Gear of Leo, Sweden.
Alstom, Västerås, Sweden.
RISE Research Institutes of Sweden, Västerås, Sweden.ORCID iD: 0000-0002-1512-0844
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2022 (English)In: Journal of Software: Evolution and Process, ISSN 2047-7473, E-ISSN 2047-7481, Vol. 34, no 11, article id e2498Article in journal (Refereed) Published
Abstract [en]

Software product line engineering emerged as an effective approach for the development of families of software-intensive systems in several industries. Although its use has been widely discussed and researched, there are still several open challenges for its industrial adoption and application. One of these is how to efficiently develop and reuse shared software artifacts, which have dependencies on the underlying electrical and hardware systems of products in a family. In this work, we report on our experience in tackling such a challenge in the railway industry and present a model-based approach for the automatic generation of test scripts for product variants in software product lines. The proposed approach is the result of an effort leveraging the experiences and results from the technology transfer activities with our industrial partner Alstom SA in Sweden. We applied and evaluated the proposed approach on the Aventra software product line from Alstom SA. The evaluation showed that the proposed approach mitigates the development effort, development time, and consistency drawbacks associated with the traditional, manual creation of test scripts. We performed an online survey involving 37 engineers from Alstom SA for collecting feedback on the approach. The result of the survey further confirms the aforementioned benefits.

Place, publisher, year, edition, pages
2022. Vol. 34, no 11, article id e2498
Keywords [en]
automation, model-based software engineering, product line engineering, testing
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:mdh:diva-60683DOI: 10.1002/smr.2498ISI: 000836874900001Scopus ID: 2-s2.0-85135404407OAI: oai:DiVA.org:mdh-60683DiVA, id: diva2:1712263
Funder
Vinnova, PANORAMAVinnova, XIVTAvailable from: 2022-11-21 Created: 2022-11-21 Last updated: 2023-09-15Bibliographically approved

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Bucaioni, AlessioSaadatmand, Mehrdad

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