Michael von Wenckstern

Behavioral compatibility of simulink models for product line maintenance and evolution

Embedded software systems, e.g. automotive, robotic or automation systems are highly configurable and consist of many software components being available in different variants and versions. To identify the degree of reusability between these different occurrences of a component, it is necessary to determine the functional backward and forward compatibility between them. Based on this information it is possible to identify in which system context a component can be replaced safely by another version, e.g. exchanging an older component, or variant, e.g. introducing new features, to achieve the same functionality. This paper presents a model checking approach to determine behavioral compatibility of Simulink models, obtained from different component variants or during evolution. A prototype for automated compatibility checking demonstrates its feasibility. In addition implemented optimizations make the analysis more efficient, when the compared variants or versions are structurally similar. A case study on a driver assistance system provided by Daimler AG shows the effectiveness of the approach to automatically compare Simulink components.

Modeling Architectures of Cyber-Physical Systems

Cyber-physical systems (CPS) in automotive or robotics industry comprise many different specific features, e.g., trajectory planning, lane correction, battery management or engine control, requiring a steady interaction with their environment over sensors and actuators. Assembling all these different features is one of the key challenges in the development of such complex systems. Component and connector (C&C) models are widely used for the design and development of CPS to represent features and their logical interaction. An advantage of C&C models is that complex features can be hierarchically decomposed into subfeatures, developed and managed by different domain experts. In this paper, we present the textual modeling family MontiCAR, Modeling and Testing of Cyber-Physical Architectures. It is based on the C&C paradigm and increases development efficiency of CPS by incorporating (i) component and connector arrays, (ii) name and index based autoconnections, (iii) a strict type system with unit and accuracy support, as well as (iv) an advanced Math language supporting BLAS operations and matrix classifications. Arrays and their autoconnection modes allow an efficient way of modeling redundant components such as front and rear park sensors or an LED matrix system containing hundreds of single dimmable lights. The strict type system and matrix classification provide means for integrated static verification of C&C architectures at compile time minimizing bug-fixing related costs. The capabilities and benefits of the proposed language family are demonstrated by a running example of a parking assistance system.

Model-Based Development of Self-Adaptive Autonomous Vehicles using the SMARDT Methodology

Cyber-physical systems are deeply intertwined with their corresponding environment through sensors and actuators. To avoid severe accidents with surrounding objects, testing the the behavior of such systems is crucial. Therefore, this paper presents the novel SMARDT (Specification Methodology Applicable to Requirements, Design, and Testing) approach to enable automated test generation based on the requirement specification and design models formalized in SysML. This paper presents and applies the novel SMARDT methodology to develop a self-adaptive software architecture dealing with controlling, planning, environment understanding, and parameter tuning. To formalize our architecture we employ a recently introduced homogeneous model-driven approach for component and connector languages integrating features indispensable in the cyber-physical systems domain. In a compelling case study we show the model driven design of a self-adaptive vehicle robot based on a modular and extensible architecture.

Test-driven semantical similarity analysis for software product line extraction

Software product line engineering rests upon the assumption that a set of products share a common base of similar functionality. The correct identification of similarities between different products can be a time-intensive task. Hence, this paper proposes an automated semantical similarity analysis supporting software product line extraction and maintenance. Under the assumption of an already identified compatible interface, the degree of semantical similarity is identified based on provided test cases. Therefore, the analysis can also be applied in a test-driven development. This is done by translating available test sequences for both components into two I/O extended finite automata and performing an abstraction of the defined behavior until a simulation relation is established. The test-based approach avoids complexity issues regarding the state space explosion problem, a common issue in model checking. The proposed approach is applied on different variants and versions of industrially used software components provided by an automotive supplier to demonstrate the methods applicability.

Fast Simulation Preorder Algorithm

Automotive industry uses model checking approaches to ensure behavioral backward compatibility of different variants and versions of software components to enable higher re-usability. Due to the lack of scalability, our already presented backward model-checking approach only allowed compatibility checks for small and midsize components. Therefore, this paper presents several optimizations, such as normalizing and hashing the Expression Abstract Syntax Tree for faster evaluations and the creation of mappings for internal Simulink variables to avoid the need to unfold them. These optimizations lead to a tremendous decrease in execution time of our backward-compatibility checks between MATLAB Simulink components enabling the support of larger models. Besides describing the methodology behind the new fast simulation preorder algorithm, this paper also evaluates the different steps of the new algorithms for a driver assistant system provided by Daimler AG.

Component and Connector Views in Practice: An Experience Report

Component and Connector (C&C) view specifications, with corresponding verification and synthesis techniques, have been recently suggested as a means for formal yet intuitive structural specification of C&C models. In this paper we report on our recent experience in applying C&C views in industrial practice, where we aimed to answer questions such as: could C&C views be practically used in industry, what are challenges of systems engineers that the use of C&C views could address, and what are some of the technical obstacles in bringing C&C views to the hands of systems engineers. We describe our experience in detail and discuss a list of lessons we have learned, including, e.g., a missing abstraction concept in C&C models and C&C views that we have identified and added to the views language and tool, that engineers can create graphical C&C views quite easily, and how verification algorithms scale on real-size industry models. Furthermore, we report on the non-negligible technical effort needed to translate Simulink block diagrams to C&C models. We make all materials mentioned and used in our experience electronically available for inspection and further research.

Highly-Optimizing and Multi-Target Compiler for Embedded System Models: C++ Compiler Toolchain for the Component and Connector Language EmbeddedMontiArc

Component and Connector (C&C) models, with their corresponding code generators, are widely used by large automotive manufacturers to develop new software functions for embedded systems interacting with their environment; C&C example applications are engine control, remote parking pilots, and traffic sign assistance. This paper presents a complete toolchain to design and compile C&C models to highly-optimized code running on multiple targets including x86/x64, ARM and WebAssembly. One of our contributions are algebraic and threading optimizations to increase execution speed for computationally expensive tasks. A further contribution is an extensive case study with over 50 experiments. This case study compares the runtime speed of the generated code using different compilers and mathematical libraries. These experiments showed that programs produced by our compiler are at least two times faster than the ones compiled by MATLAB/Simulink for machine learning applications such as image clustering for object detection. Additionally, our compiler toolchain provides a complete model-based testing framework and plug-in points for middleware integration. We make all materials including models and toolchains electronically available for inspection and further research.