Set-Based Concurrent Engineering Model for Automotive Electronic/Software Systems Development

Organised by: Cranfield UniversityThis paper is presenting a proposal of a novel approach to automotive electronic/software systems development. It is based on the combination of Set-Based Concurrent Engineering, a Toyota approach to product development, with the standard V-Model of software development. Automotive industry currently faces the problem of growing complexity of electronic/software systems. This issue is especially visible at the level of integration of these systems which is difficult and error-prone. The presented conceptual proposal is to establish better processes that could handle the electronic/software systems design and development in a more integrated and consistent manner.Mori Seiki – The Machine Tool Compan

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

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

Assisted assignment of automotive safety requirements

ISO 26262, a functional-safety standard, uses Automotive Safety Integrity Levels (ASILs) to assign safety requirements to automotive-system elements. System designers initially assign ASILs to system-level hazards and then allocate them to elements of the refined system architecture. Through ASIL decomposition, designers can divide a function & rsquo;s safety requirements among multiple components. However, in practice, manual ASIL decomposition is difficult and produces varying results. To overcome this problem, a new tool automates ASIL allocation and decomposition. It supports the system and software engineering life cycle by enabling users to efficiently allocate safety requirements regarding systematic failures in the design of critical embedded computer systems. The tool is applicable to industries with a similar concept of safety integrity levels. © 1984-2012 IEEE

20th Workshop on Automotive Software Engineering (ASE’23)

Software-based systems play an increasingly important role and enable most innovations in modern cars. This workshop will address various topics related to automotive software development. The participants will discuss appropriate methods, techniques, and tools needed to address the most current challenges for researchers and practitioners

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

Why and How Your Traceability Should Evolve: Insights from an Automotive Supplier

Traceability is a key enabler of various activities in automotive software and systems engineering and required by several standards. However, most existing traceability management approaches do not consider that traceability is situated in constantly changing development contexts involving multiple stakeholders. Together with an automotive supplier, we analyzed how technology, business, and organizational factors raise the need for flexible traceability. We present how traceability can be evolved in the development lifecycle, from early elicitation of traceability needs to the implementation of mature traceability strategies. Moreover, we shed light on how traceability can be managed flexibly within an agile team and more formally when crossing team borders and organizational borders. Based on these insights, we present requirements for flexible tool solutions, supporting varying levels of data quality, change propagation, versioning, and organizational traceability.Comment: 9 pages, 3 figures, accepted in IEEE Softwar