Enabling automated integration of architectural languages: An experience report from the automotive domain

Abstract

Modern automotive software systems consist of hundreds of heterogeneous software applications, belonging to separated function domains and often developed within distributed automotive ecosystems consisting of original equipment manufactures, tier-1 and tier-2 companies. Hence, the development of modern automotive software systems is a formidable challenge. A well-known instrument for coping with the tremendous heterogeneity and complexity of modern automotive software systems is the use of architectural languages as a way of enabling different and specific views over these systems. However, the use of different architectural languages might come with the cost of reduced interoperability and automation as different languages might have weak to no integration. In this article, we tackle the challenge of integrating two architectural languages heavily used in the automotive domain for the design and timing analysis of automotive software systems: AMALTHEA and Rubus Component Model. The main contributions of this paper are (i) a mapping scheme for the translation of an AMALTHEA architecture into a Rubus Component Model architecture where high-precision timing analysis can be run, and the back annotation of the analysis results on the starting AMALTHEA architecture; (ii) the implementation of the proposed scheme, which uses the concept of model transformations for enabling a full-fledged automated integration; (iii) the application of such automation on three industrial automotive systems being the brake-by-wire, the full blown engine management system and the engine management system. We discuss and evaluate the proposed contributions using an online, experts survey and the above-mentioned use cases. Based on the evaluation results, we conclude that the proposed automation mechanism is correct and applicable in industrial contexts. Besides, we observe that the performance of the automation mechanism does not degrade when translating large models with several thousands of elements. Eventually, we conclude that experts in this field find the proposed contribution industrially relevant.