Automatically Learning Formal Models from Autonomous Driving Software

The correctness of autonomous driving software is of utmost importance, as incorrect behavior may have catastrophic consequences. Formal model-based engineering techniques can help guarantee correctness and thereby allow the safe deployment of autonomous vehicles. However, challenges exist for widespread industrial adoption of formal methods. One of these challenges is the model construction problem. Manual construction of formal models is time-consuming, error-prone, and intractable for large systems. Automating model construction would be a big step towards widespread industrial adoption of formal methods for system development, re-engineering, and reverse engineering. This article applies active learning techniques to obtain formal models of an existing (under development) autonomous driving software module implemented in MATLAB. This demonstrates the feasibility of automated learning for automotive industrial use. Additionally, practical challenges in applying automata learning, and possible directions for integrating automata learning into the automotive software development workflow, are discussed

Managed and Continuous Evolution of Dependable Automotive Software Systems / Andreas Rausch, Oliver Brox, Axel Grewe, Marcel Ibe, Stefanie Jauns-Seyfried, Christoph Knieke, Marco Körner, Steffen Küpper, Malte Mauritz, Henrik Peters, Arthur Strasser, Martin Vogel, Norbert Weiss

Automotive software systems are an essential and innovative part of nowadays connected and automated vehicles. Automotive industry is currently facing the challenge to re-invent the automobile. Consequently, automotive software systems, their software systems architecture, and the way we engineer those kinds of software systems are confronted with major challenges: managing complexity, providing flexibility, and guaranteeing dependability of the desired automotive software systems and the corresponding engineering process. In this paper we will present an improved and sophisticated engineering approach. Our approach is based on the managed and continuous evolution of dependable automotive software systems. It helps engineers to manage system complexity based on continous engineering processes to iteratively evolve automotive software systems and therby guarantee the required dependability issues. Based on a running sample, we will present and illustrate the main assets of the proposed engineering approach for managed and continuous evolution of dependable automotive software systems

Managed and Continuous Evolution of Dependable Automotive Software Systems / Andreas Rausch, Oliver Brox, Axel Grewe, Marcel Ibe, Stefanie Jauns-Seyfried, Christoph Knieke, Marco Körner, Steffen Küpper, Malte Mauritz, Henrik Peters, Arthur Strasser, Martin Vogel, Norbert Weiss

Automotive software systems are an essential and innovative part of nowadays connected and automated vehicles. Automotive industry is currently facing the challenge to re-invent the automobile. Consequently, automotive software systems, their software systems architecture, and the way we engineer those kinds of software systems are confronted with major challenges: managing complexity, providing flexibility, and guaranteeing dependability of the desired automotive software systems and the corresponding engineering process. In this paper we will present an improved and sophisticated engineering approach. Our approach is based on the managed and continuous evolution of dependable automotive software systems. It helps engineers to manage system complexity based on continous engineering processes to iteratively evolve automotive software systems and therby guarantee the required dependability issues. Based on a running sample, we will present and illustrate the main assets of the proposed engineering approach for managed and continuous evolution of dependable automotive software systems

Defining Archetypes of E-Collaboration for Product Development in The Automotive Industry

The automotive industry represents one of the most relevant industrial sectors of the global economy. In response to a plethora of challenges, e-collaboration for product development has become a nexus of competitive advantage in the automotive world. Since new dynamics in organizational forms on the one hand and advancements in engineering information systems on the other hand have led to increased complexity, a classification model to organize and structure the manifold manifestations seems analytically useful. Hence, the paper at hand (1) proposes, (2) describes, and (3) validates archetypes of e-collaboration for product development in the automotive industry. Anchored in (1) a structured literature review and (2) rich empirical evidence from a multiple-case study in the automotive ecosystem, we organize our research study along a well-established, two-stage research method on archetypes adopting a socio-technical systems perspective. Key findings include the archetypes (1) mechanical development-dominant, (2) software development-dominant, (3) systems engineering-oriented, and (4) non-development-focused e-collaborations for product development as basic patterns. Thereby, “importance of mechanical development” and “importance of software development” act as essential classification dimensions. Keeping the inherent limitations of the qualitative research tradition in mind, this paper offers theoretical, methodological, managerial, and cross-disciplinary contributions

Automotive embedded systems software reprogramming

This thesis was submitted for the degree of Doctor of Philosophy and was awarded by Brunel UniversityThe exponential growth of computer power is no longer limited to stand alone computing systems but applies to all areas of commercial embedded computing systems. The ongoing rapid growth in intelligent embedded systems is visible in the commercial automotive area, where a modern car today implements up to 80 different electronic control units (ECUs) and their total memory size has been increased to several hundreds of megabyte. This growth in the commercial mass production world has led to new challenges, even within the automotive industry but also in other business areas where cost pressure is high. The need to drive cost down means that every cent spent on recurring engineering costs needs to be justified. A conflict between functional requirements (functionality, system reliability, production and manufacturing aspects etc.), testing and maintainability aspects is given. Software reprogramming, as a key issue within the automotive industry, solve that given conflict partly in the past. Software Reprogramming for in-field service and maintenance in the after sales markets provides a strong method to fix previously not identified software errors. But the increasing software sizes and therefore the increasing software reprogramming times will reduce the benefits. Especially if ECU’s software size growth faster than vehicle’s onboard infrastructure can be adjusted. The thesis result enables cost prediction of embedded systems’ software reprogramming by generating an effective and reliable model for reprogramming time for different existing and new technologies. This model and additional research results contribute to a timeline for short term, mid term and long term solutions which will solve the currently given problems as well as future challenges, especially for the automotive industry but also for all other business areas where cost pressure is high and software reprogramming is a key issue during products life cycle

Online experimentation in automotive software engineering

Context: Online experimentation has long been the gold standard for evaluating software towards the actual needs and preferences of customers. In the Software-as-a-Service domain, various online experimentation techniques are applied and proven successful. As software is becoming the main differentiator for automotive products, the automotive sector has started to express an interest in adopting online experimentation to strengthen their software development process. Objective: In this research, we aim to systematically address the challenges in adopting online experimentation in the automotive domain.Method: We apply a multidisciplinary approach to this research. To understand the state-of-practise in online experimentation in the industry, we conduct case studies with three manufacturers. We introduce our experimental design and evaluation methods to real vehicles driven by customers at scale. Moreover, we run experiments to quantitatively evaluate experiment design and causal inference models. Results: Four main research outcomes are presented in this thesis. First, we propose an architecture for continuous online experimentation given the limitations experienced in the automotive domain. Second, after identifying an inherent limitation of sample sizes in the automotive domain, we apply and evaluate an experimentation design method. The method allows us to utilise pre-experimental data for generating balanced groups even when sample sizes are limited. Third, we present an alternative approach to randomised experiments and demonstrate the application of Bayesian causal inference in online software evaluation. With the models, we enable software online evaluation without the need for a fully randomised experiment. Finally, we relate the formal assumption in the Bayesian causal models to the implications in practise, and we demonstrate the inference models with cases from the automotive domain. Outlook: In our future work, we plan to explore causal structural and graphical models applied in software engineering, and demonstrate the application of causal discovery in machine learning-based autonomous drive software