Towards flexible and efficient model-based testing, utilizing domain-specific modelling

Olli-Pekka Puolitaival, Teemu Kanstrén

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

5 Citations (Scopus)

Abstract

Model-Based Testing is a test automation technique that generates test cases based on a model of the system under test. Domain-specific modelling is a modelling approach where the developed system is modelled in terms of domain-specific concepts and these models are automatically transformed to other forms such as application code. In this paper, we will discuss the adoption and integration of domain-specific modelling with model-based testing tools. Since model-based testing tools utilise various modelling notations that typically diverge from a specific domain-model, we will discuss how domain specific models can be automatically transformed to become suitable models for a chosen model-based testing tool. Furthermore, by doing this in terms of a domain-specific meta-model, we will allow one to switch between various model-based testing tools.
Original languageEnglish
Title of host publicationProceedings of the 10th Workshop on Domain-Specific Modeling, DSM'10
PublisherAssociation for Computing Machinery ACM
ISBN (Print)978-1-4503-0549-5
DOIs
Publication statusPublished - 2010
MoE publication typeA4 Article in a conference publication
Event10th Workshop on Domain-Specific Modeling, DSM'10 - Reno, Nevada, United States
Duration: 17 Oct 201018 Oct 2010

Conference

Conference10th Workshop on Domain-Specific Modeling, DSM'10
Abbreviated titleDSM 2010
CountryUnited States
CityReno, Nevada
Period17/10/1018/10/10

    Fingerprint

Keywords

  • Domain-specific modelling
  • model-based testing
  • meta-model

Cite this

Puolitaival, O-P., & Kanstrén, T. (2010). Towards flexible and efficient model-based testing, utilizing domain-specific modelling. In Proceedings of the 10th Workshop on Domain-Specific Modeling, DSM'10 [8] Association for Computing Machinery ACM. https://doi.org/10.1145/2060329.2060349