https://www.mdu.se/

Considering the ubiquitousness of software in modern vehicles, its increased value and development cost, an efficient software development became of paramount importance for the vehicular domain. It has been identified that early verification of non functional properties of  vehicular embedded software such as, timing, reliability and safety, is crucial to efficiency. However, early verification of non functional properties is hard to achieve with traditional software development approaches due to the abstraction and the lack of automation of these methodologies.

This doctoral thesis aims at improving efficiency in vehicular embedded software development by minimising the need for late, expensive and time consuming software modifications with early design changes, identified through timing verification, which usually are cheaper and faster. To this end, we introduce a novel model-driven approach which exploits the interplay of two automotive-specific modelling languages for the representation of functional and execution models and defines a suite of model transformations for their automatic integration.

Starting from a functional model (expressed by means of EAST-ADL), all the execution models (expressed by means of the Rubus Component Model) entailing unique timing configurations are derived. Schedulability analysis selects the set of the feasible execution models with respect to specified timing requirements. Eventually, a reference to the selected execution models along with their analysis results is automatically created in the related functional model to allow the engineer to investigate them.

The main scientific contributions of this doctoral thesis are i) a metamodel definition for the Rubus Component Model, ii) an automatic mechanism for the generation of Rubus models from EAST-ADL, iii) an automatic mechanism for the selection and back-propagation of the analysis results and related Rubus models to design level and iv) a compact notation for visualising the selected Rubus models by means of a single execution model.