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Use, Potential, and Showstoppers of Models in Automotive Requirements Engineering

Artikel i vetenskaplig tidskrift
Författare Grischa Liebel
Matthias Tichy
Eric Knauss
Publicerad i Journal of Software and Systems Modeling
Volym 18
Sidor 2587–2607
ISSN 1619-1366
Publiceringsår 2019
Publicerad vid Institutionen för data- och informationsteknik (GU)
Sidor 2587–2607
Språk en
Länkar dx.doi.org/10.1007/s10270-018-0683-...
Ämnesord Modelling; MDE; MBE; Requirements Engineering; Empirical Research; Case Study
Ämneskategorier Programvaruteknik


Several studies report that the use of model-centric methods in the automotive domain is widespread and offers several benefits. However, existing work indicates that few modelling frameworks explicitly include requirements engineering (RE), and that natural language descriptions are still the status quo in RE. Therefore, we aim to increase the understanding of current and potential future use of models in RE, with respect to the automotive domain. In this paper, we report our findings from a multiple-case study with two automotive companies, collecting interview data from 14 practitioners. Our results show that models are used for a variety of different purposes during RE in the automotive domain, e.g., to improve communication and to handle complexity. However, these models are often used in an unsystematic fashion and restricted to few experts. A more widespread use of models is prevented by various challenges, most of which align with existing work on model use in a general sense. Furthermore, our results indicate that there are many potential benefits associated with future use of models during RE. Interestingly, existing research does not align well with several of the proposed use cases, e.g., restricting the use of models to informal notations for communication purposes. Based on our findings, we recommend a stronger focus on informal modelling and on using models for multi-disciplinary environments. Additionally, we see the need for future work in the area of model use, i.e., information extraction from models by non-expert modellers.

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