From Simulation Data to Test Cases for Fully Automated Driving and ADAS

Part 3: Practical Applications International audience 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). Document type: Part of book or chapter of boo

2nd International Workshop on Automotive Systems and Software Architectures (WASA)—Introduction to special section

Automotive software engineering was officially introduced more than a decade ago into the software community addressing research challenges and technical issues encountering software development in the automotive domain. A modern-day premium class vehicle contains up to 100 000 000 lines of executable code running on multiple microcontrollers creating a heterogeneous, deeply coupled networking system. Nowadays trends like car2x, (fully) electric vehicles or self-driving cars are all based on software. Those new features will require new engineering approaches and more advanced software architectures suitable for automotive domain

2nd International Workshop on Automotive Systems and Software Architectures (WASA)—Introduction to special section

Automotive software engineering was officially introduced more than a decade ago into the software community addressing research challenges and technical issues encountering software development in the automotive domain. A modern-day premium class vehicle contains up to 100 000 000 lines of executable code running on multiple microcontrollers creating a heterogeneous, deeply coupled networking system. Nowadays trends like car2x, (fully) electric vehicles or self-driving cars are all based on software. Those new features will require new engineering approaches and more advanced software architectures suitable for automotive domain

Novel Insights on Cross Project Fault Prediction Applied to Automotive Software

Part 3: Monitoring and Fault LocalizationInternational audienceDefect prediction is a powerful tool that greatly helps focusing quality assurance efforts during development. In the case of the availability of fault data from a particular context, there are different ways of using such fault predictions in practice. Companies like Google, Bell Labs and Cisco make use of fault prediction, whereas its use within automotive industry has not yet gained a lot of attraction, although, modern cars require a huge amount of software to operate. In this paper, we want to contribute the adoption of fault prediction techniques for automotive software projects. Hereby we rely on a publicly available data set comprising fault data from three automotive software projects. When learning a fault prediction model from the data of one particular project, we achieve a remarkably high and nearly perfect prediction performance for the same project. However, when applying a cross-project prediction we obtain rather poor results. These results are rather surprising, because of the fact that the underlying projects are as similar as two distinct projects can possibly be within a certain application context. Therefore we investigate the reasons behind this observation through correlation and factor analyses techniques. We further report the obtained findings and discuss the consequences for future applications of Cross-Project Fault Prediction (CPFP) in the domain of automotive software

From Simulation Data to Test Cases for Fully Automated Driving and ADAS - A Multilayer Model Concept

Within this paper we present a new concept on deriving test cases from simulation data and outline challenging tasks when fully 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 highly automated vehicles are given. Well known criticality metrics are assessed and discussed with regard to their potential to test fully automated vehicles and ADAS. Most of them are not applicable to identity relevant trafic 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 build up and linked to system requirements to derive test cases using a Domain Specific Language (DSL)

Tailoring complexity metrics for simulink models

The 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