Within this paper we present a new concept on deriving test cases from simulation data and outline challenging tasks when testing and validating fully automated driving functions and Advanced Driver Assistance Systems (ADAS). Open questions on topics like virtual simulation and identification of relevant situations for consistent testing of fully automated vehicles are given. Well known criticality metrics are assessed and discussed with regard to their potential to test fully automated vehicles and ADAS. Upon our knowledge most of them are not applicable to identify relevant traffic situations which are of importance for fully automated driving and ADAS. To overcome this limitation, we present a concept including filtering and rating of potentially relevant situations. Identified situations are described in a formal, abstract and human readable way. Finally, a situation catalogue is built up and linked to system requirements to derive test cases using a Domain Specific Language (DSL).
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ABSTRACTThe size and complexity of Simulink models is constantly increasing, just as the systems which they represent. Therefore, it is beneficial to control them already at the design phase. In this paper we establish a set of complexity metrics for Simulink models to capture diverse aspects of complexity by proposing new and redefining existing metrics. To evaluate the applicability of our metrics, we compare them with the closed-source metric proposed by Mathworks. Moreover, through a case study from the automotive domain, we relate such metrics to quality attributes as determined by domain experts, and correlate them to known faults. Preliminary assessment suggests that complexity is closely related to analysability, understandability, and testability.
In this paper, we present a novel industry dataset on static software and change metrics for Matlab/Simulink models and their corresponding auto-generated C source code. The data set comprises data of three automotive projects developed and tested accordingly to industry standards and restrictive software development guidelines. We present some background information of the projects, the development process and the issue tracking as well as the creation steps of the dataset and the used tools during development. A specific highlight of the dataset is a low measurement error on change metrics because of the used issue tracking and commit policies.
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