Modern automobiles have evolved from just being mechanical machines to having full-fledged electronics systems that enhance vehicle dynamics and driver experience. However, these complex hardware and software systems, if not properly designed, can experience failures that can compromise the safety of the vehicle, its occupants, and the surrounding environment. For example, a system to activate the brakes to avoid a collision saves lives when it functions properly, but could lead to tragic outcomes if the brakes were applied in a way that's inconsistent with the design. Broadly speaking, the analysis performed to minimize such risks falls into a systems engineering domain called Functional Safety. In this paper, we present SafetyLens, a visual data analysis tool to assist engineers and analysts in analyzing automotive Functional Safety datasets. SafetyLens combines techniques including network exploration and visual comparison to help analysts perform domain-specific tasks. This paper presents the design study with domain experts that resulted in the design guidelines, the tool, and user feedback.

Embedded systems, with their tight technology integration, and multiple requirements and stakeholders, are characterized by tightly interrelated processes, information and tools. Embedded systems will as a consequence be described by multiple, heterogeneous and interrelated descriptions such as for example requirements documents, design and analysis models, software and hardware descriptions. We refer to a system designed this way as a multi-view (MV) system. The main contribution of this paper is a characterization of model-based approaches to MV systems. The characterization takes three main perspectives for the relations between viewpoints: semantic relations (content), relations over time (process), and manipulation of views (operations). We complement these perspectives by investigating MV system challenges and by a survey of related approaches. The characterization aims to provide a basis for a better understanding, design and implementation of MV systems, and thereby to overcome the current fragmented points of view on integrated multi-view modeling (MVM).