Continuous Experimentation for Automotive Software on the Example of a Heavy Commercial Vehicle in Daily Operation

As the automotive industry focuses its attention more and more towards the software functionality of vehicles, techniques to deliver new software value at a fast pace are needed. Continuous Experimentation, a practice coming from the web-based systems world, is one of such techniques. It enables researchers and developers to use real-world data to verify their hypothesis and steer the software evolution based on performances and user preferences, reducing the reliance on simulations and guesswork. Several challenges prevent the verbatim adoption of this practice on automotive cyber-physical systems, e.g., safety concerns and limitations from computational resources; nonetheless, the automotive field is starting to take interest in this technique. This work aims at demonstrating and evaluating a prototypical Continuous Experimentation infrastructure, implemented on a distributed computational system housed in a commercial truck tractor that is used in daily operations by a logistic company on public roads. The system comprises computing units and sensors, and software deployment and data retrieval are only possible remotely via a mobile data connection due to the commercial interests of the logistics company. This study shows that the proposed experimentation process resulted in the development team being able to base software development choices on the real-world data collected during the experimental procedure. Additionally, a set of previously identified design criteria to enable Continuous Experimentation on automotive systems was discussed and their validity confirmed in the light of the presented work.Comment: Paper accepted to the 14th European Conference on Software Architecture (ECSA 2020). 16 pages, 5 figure

Bridging the Experimental Gap: Applying Continuous Experimentation to the Field of Cyber-Physical Systems, in the Example of the Automotive Domain

In the software world frequent updates and fast delivery of new features are needed by companies to bring value to customers and not lag behind competition. When in cyber-physical systems the software functionality dominates in importance the hardware capabilities, the same speed in creating new value is needed by the product owners to differentiate their products and attract customers. The automotive field is an example of a domain that will face this challenge as the industry races to achieve self-driving vehicles, which will necessarily be software-intensive highly complex cyber-physical systems. A software engineering practice capable of accelerating and guiding the software production process using real-world data is Continuous Experimentation. This practice proved to be valuable in software-intensive web-based systems, allowing data-driven software evolution. It involves the use of experiments, which are instrumented versions of the software to be tested, deployed to the actual systems and executed in a limited way alongside the official software version. Valuable data on the future behavior of the prospective feature is collected in this way as it was fed the same real-world data it would encounter once approved and deployed. Additionally, in those cases where an experimental software version can be run as a replacement for the official version, relevant data regarding the system-user interaction can be gathered. In this thesis, the field of cyber-physical systems and the automotive practitioners\u27 perspective on Continuous Experimentation are sampled employing a literature review and a series of case studies. A set of necessary architectural characteristics are defined and possible methods to overcome the issue of resource constraints in cyber-physical systems are proposed in two exploratory studies. Finally, a design study shows and analyses a prototype of a Continuous Experimentation cycle that was designed and executed in a project partnered by Revere, the Chalmers University of Technology\u27s laboratory for vehicle research

Experimenting with Risk and Management Control Systems in Inter-firm Alliances

This thesis examines the interrelations between risk and management control systems in inter-firm alliances—specifically, how risks influence the choices and construction of management control systems, and how these management control systems subsequently affect the articulation and evolution of risks. The research issues are examined by using the theoretical perspective of a three-stage experimentation process under the actor-network theory. Data were collected from two international joint ventures in the Chinese automobile industry through formal and informal interviews, observations and review of internal and external documents. This study found that risk and inter-firm alliances continuously and mutually construct each other through management control systems. On the one hand, management control systems contribute to the articulation of risks in inter-firm alliances by identifying the source of risk, generating risk perception, rationalising and objectifying risk perception, and producing knowledge on risk. On the other hand, different management control systems are initiated, crafted, selected, formalised and negotiated as the outcome of the partner firm’s efforts to experiment with solutions to address risks. These findings contribute to the accounting and management control literature by demonstrating that risk management in inter-firm alliances is not a static phenomenon. Instead, risk and management control systems in inter-firm alliances are (re)constructed through an ongoing experimentation process that involves interactions among firms in crafting and testing solutions to risks. In this ongoing experimentation process, management control systems are not only used to manage risks, but also contribute to the articulation of new risks. This thesis also extends prior research by highlighting how personal ties may play a critical role in the risk management of alliance relationships. Overall, this thesis demonstrates the practice of forming and deploying multiple alliances to create an alliance portfolio in order to manage the dynamics of risks in inter-firm alliances

Paving the way to electrified road transport - Publicly funded research, development and demonstration projects on electric and plug-in vehicles in Europe

The electrification of road transport or electro-mobility is seen by many as a potential game-changing technology that could have a significant influence on the future cost and environmental performance of personal individual mobility as well as short distance goods transport. While there is currently a great momentum vis-à-vis electro-mobility, it is yet unclear, if its deployment is economically viable in the medium to long term. Electromobility, in its early phase of deployment, still faces significant hurdles that need to be overcome in order to reach a greater market presence. Further progress is needed to overcome some of these hurdles. The importance of regulatory and financial support to emerging environmentally friendly transport technologies has been stressed in multiple occasions. The aim of our study was to collect the information on all on-going or recently concluded research, development and demonstration projects on electric and plug-in hybrid electric vehicles, which received EU or national public funding with a budget >1mln Euro, in order to assess which of the electric drive vehicles (EDV) challenges are addressed by these projects and to identify potential gaps in the research, development, and demonstration (R, D & D) landscape in Europe. The data on R, D & D projects on electric and plug-in vehicles, which receive public funding, has been collected by means of (i) on-line research, (ii) validation of an inventory of projects at member state level through national contacts and (iii) validation of specific project information through distribution of project information templates among project coordinators. The type of information which was gathered for the database included: EDV component(s) targeted for R&D, location and scope of demo projects, short project descriptions, project budget and amount of public co-funding received, funding organisation, project coordinator,number and type of partners (i.e. utilities, OEMs, services, research institutions, local authorities), start and duration of the project. The validation process permitted the identification of additional projects which were not accounted for in the original online search. Statistical elaboration of the collected data was conducted. More than 320 R, D & D projects funded by the EU and Member states are listed and analyzed. Their total budgets add up to approximately 1.9 billion Euros. Collected data allowed also the development of an interactive emobility visualization tool, called EV-Radar, which portrays in an interactive way R&D and demonstration efforts for EDVs in Europe. It can be accessed under http://iet.jrc.ec.europa.eu/ev-radar.JRC.F.6-Energy systems evaluatio

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

Automotive UX design and data-driven development: Narrowing the gap to support practitioners

The development and evaluation of In-Vehicle Information Systems (IVISs) is strongly based on insights from qualitative studies conducted in artificial contexts (e.g., driving simulators or lab experiments). However, the growing complexity of the systems and the uncertainty about the context in which they are used, create a need to augment qualitative data with quantitative data, collected during real-world driving. In contrast to many digital companies that are already successfully using data-driven methods, Original Equipment Manufacturers (OEMs) are not yet succeeding in releasing the potentials such methods offer. We aim to understand what prevents automotive OEMs from applying data-driven methods, what needs practitioners formulate, and how collecting and analyzing usage data from vehicles can enhance UX activities. We adopted a Multiphase Mixed Methods approach comprising two interview studies with more than 15 UX practitioners and two action research studies conducted with two different OEMs. From the four studies, we synthesize the needs of UX designers, extract limitations within the domain that hinder the application of data-driven methods, elaborate on unleveraged potentials, and formulate recommendations to improve the usage of vehicle data. We conclude that, in addition to modernizing the legal, technical, and organizational infrastructure, UX and Data Science must be brought closer together by reducing silo mentality and increasing interdisciplinary collaboration. New tools and methods need to be developed and UX experts must be empowered to make data-based evidence an integral part of the UX design process