Dimitri Plotnikov

A comparison of mechanisms for integrating handwritten and generated code for object-oriented programming languages

Code generation from models is a core activity in model-driven development (MDD). For complex systems it is usually impossible to generate the entire software system from models alone. Thus, MDD requires mechanisms for integrating generated and handwritten code. Applying such mechanisms without considering their effects can cause issues in projects with many model and code artifacts, where a sound integration for generated and handwritten code is necessary. We provide an overview of mechanisms for integrating generated and handwritten code for object-oriented languages. In addition to that, we define and apply criteria to compare these mechanisms. The results are intended to help MDD tool developers in choosing an appropriate integration mechanism.

Integration of Handwritten and Generated Object-Oriented Code

In many development projects models are core artifacts used to generate concrete implementations from them. However, for many systems it is impossible or not useful to generate the complete software system from models alone. Hence, developers need mechanisms for integrating generated and handwritten code. Applying such mechanisms without considering their effects can cause issues in projects, where model and code artifacts are essential. Thus, a sound approach for the integration of both forms of code is needed.

Large-Scale Evaluation of an Active Safety Algorithm with EuroNCAP and US NCAP Scenarios in a Virtual Test Environment -- An Industrial Case Study

Context: Recently, test protocols from organizations like European New Car Assessment Programme (EuroNCAP) were extended to also cover active safety systems. Objective: The official EuroNCAP test protocol for Autonomous Emergency Braking (AEB)/Forward Collision Warning (FCW) systems explicitly defines to what extent a Vehicle-Under-Test (VUT) is allowed to vary in its lateral position. In addition, the United States New Car Assessment Programme (US NCAP) test protocol has broader tolerance ranges. The goal for automotive OEMs is to understand the impact of such allowed variations on a the overall vehicles performance. Method: A simulation-based approach is outlined that allows systematic, large-scale analysis of such influences to effectively plan time-consuming and resource-intense real-world vehicle tests. Our models allow a profound analysis of an AEB algorithm by modeling and conducting more than 3,000 simulation runs with EuroNCAPs dynamic CCRm and CCRb scenarios including those with adopted USNCAP parameters. Results: Our structured analysis of such test procedures involving dynamic actors is the first of its kind in a relevant industrial setting. Several anomalies were unveiled under US NCAP conditions to support real-world test runs. Hence, we could show that the proposed method supports all possible scenarios in AEB consumer tests and scales as we had to timely process approx. 7.7GB of simulation data. Conclusion: To achieve the expected performance and to study a systems behavior in potential misuse cases from a functional point of view, large scale, model-based simulations complement traditional testing on proving ground.

Simulations on Consumer Tests: A Systematic Evaluation Approach in an Industrial Case Study

Context: Consumer tests which assess safety features of modern vehicles have a tradition in Europe. Recently, such test protocols have been substantially extended to also cover active safety systems like Volkswagens Front Assist.

Automatically Selecting a Suitable Integration Scheme for Systems of Differential Equations in Neuron Models

On the level of the spiking activity, the integrate-and-fire neuron is one of the most commonly used descriptions of neural activity. A multitude of variants has been proposed to cope with the huge diversity of behaviors observed in biological nerve cells. The main appeal of this class of model is that it can be defined in terms of a hybrid model, where a set of mathematical equations describes the sub-threshold dynamics of the membrane potential and the generation of action potentials is often only added algorithmically without the shape of spikes being part of the equations. In contrast to more detailed biophysical models, this simple description of neuron models allows the routine simulation of large biological neuronal networks on standard hardware widely available in most laboratories these days. The time evolution of the relevant state variables is usually defined by a small set of ordinary differential equations (ODEs). A small number of evolution schemes for the corresponding systems of ODEs are commonly used for many neuron models, and form the basis of the neuron model implementations built into commonly used simulators like Brian, NEST and NEURON. However, an often neglected problem is that the implemented evolution schemes are only rarely selected through a structured process based on numerical criteria. This practice cannot guarantee accurate and stable solutions for the equations and the actual quality of the solution depends largely on the parametrization of the model. In this article, we give an overview of typical equations and state descriptions for the dynamics of the relevant variables in integrate-and-fire models. We then describe a formal mathematical process to automate the design or selection of a suitable evolution scheme for this large class of models. Finally, we present the reference implementation of our symbolic analysis toolbox for ODEs that can guide modelers during the implementation of custom neuron models.