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Technical Paper

Reducing the Verification Effort for Interfaces of Automotive Infotainment Software

2015-04-14
2015-01-0166
Car infotainment systems feature an increasing number of functions to keep pace with consumer needs. The GENIVI Alliance aims to facilitate this evolution of infotainment systems by developing a common baseline where services of different suppliers can easily be integrated on a single hardware platform. Since the huge number of services creates more dependencies and interactions, more effort is required to ensure the same level of quality. We present a novel approach and effective tooling to reduce the effort for the interface verification of in-vehicle software components. Our models create different views of the system. Consistency checks and automated transformations between the views reduce the modeling effort and ensure compatible interactions of distributed software components. Layered reference models separate the description of the structure and the behavior of the services' communication.
Journal Article

Control Flow Analysis of Automotive Software Components Using Model-Based Specifications of Dynamic Behavior

2013-04-08
2013-01-0435
The amount of software in the automotive domain is steadily increasing. Different ways of abstraction are used to meet this challenge. One of the most promising approaches is Model-Based Development. Software components are currently designed and developed with model-based approaches which describe syntactical and behavioral aspects of the components. Being able to analyze the models in concern to compatibility and traceability of messages and function calls is an important goal. Especially for the behavioral models this task is complex and time consuming. In this paper we focus on the infotainment domain and present three use-cases for control flow analysis in behavioral models. The first is the compatibility analysis between components, the second is the analysis of control flow over multiple connected components and the third is the consistency analysis of sequence diagrams. We present an approach to address these tasks using model transformation and graph-search algorithms.
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