Assessing the impact of meta-model evolution: a measure and its automotive application

Abstract

Domain-specific meta-models play an important role in the design of large software systems by defining language for the architectural models. Such common modeling languages are particularly important if multiple actors are involved in the development process as they assure interoperability between modeling tools used by different actors. The main objective of this paper is to facilitate the adoption of new domain-specific meta-model versions, or a subset of the new architectural features they support, by the architectural modeling tools used by different actors in the development of large software systems. In order to achieve this objective, we developed a simple measure of meta-model evolution (named NoC—Number of Changes) that captures atomic modification between different versions of the analyzed meta-model. We evaluated the NoC measure on the evolution of the AUTOSAR meta-model, a domain-specific meta-model used in the design of automotive system architectures. The evaluation shows that the measure can be used as an indicator of effort needed to update meta-model-based tools to support different actors in modeling new architectural features. Our detailed results show the impact of 14 new AUTOSAR features on the modeling tools used by the main actors in the automotive development process. We validated our results by finding a significant correlation between the results of the NoC measure and the actual effort needed to support these features in the modeling tools reported by the modeling practitioners from four AUTOSAR tool vendors and the AUTOSAR tooling team at Volvo Cars. Generally, our study shows that quantitative analysis of domain-specific meta-model evolution using a simple measure such as NoC can be used as an indicator of the required updates in the meta-model-based tools that are needed to support new meta-model versions. However, our study also shows that qualitative analysis that may include an inspection of the actual meta-model changes is needed for more accurate assessment.