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

Architecture-driven, Multi-concern and Seamless Assurance and Certification of Cyber-Physical Systems.

Unlike practices in electrical and mechanical equipment engineering, Cyber-Physical Systems (CPS) do not have a set of standardized and harmonized practices for assurance and certification that ensures safe, secure and reliable operation with typical software and hardware architectures. This paper presents a recent initiative called AMASS (Architecture-driven, Multi-concern and Seamless Assurance and Certification of Cyber-Physical Systems) to promote harmonization, reuse and automation of labour-intensive certification-oriented activities via using model-based approaches and incremental techniques. AMASS will develop an integrated and holistic approach, a supporting tool ecosystem and a self-sustainable community for assurance and certification of CPS. The approach will be driven by architectural decisions (fully compatible with standards, e.g. AUTOSAR and IMA), including multiple assurance concerns such as safety, security and reliability. AMASS will support seamless interoperability between assurance/certification and engineering activities along with third-party activities (external assessments, supplier assurance). The ultimate aim is to lower certification costs in face of rapidly changing product features and market needs.This project has received funding from the Electronic Component Systems for European Leadership Joint Undertaking under grant agreement No 692474. This Joint Undertaking receives support from the European Union’s Horizon 2020 research and innovation programme and Spain, Czech Republic, Germany, Sweden, Austria, Italy, United Kingdom, Franc

Incremental Latency Analysis of Heterogeneous Cyber-Physical Systems

REACTION 2014. 3rd International Workshop on Real-time and Distributed Computing in Emerging Applications. Rome, Italy. December 2nd, 2014.Cyber-Physical Systems, as used in automotive, avionics, or aerospace domains, have critical real-time require-ments. Time-related issues might have important impacts and, as these systems are becoming extremely software-reliant, validate and enforcing timing constraints is becoming difficult. Current techniques are mainly focused on validating these constraints late by using integration tests and tracing the system execution. Such methods are time-consuming and labor-intensive and, discovering timing issue late in the development process might incur significant rework efforts. In this paper, we propose an incremental model-based ap-proach to analyze and validate timing requirements of cyber-physical systems. We first capture the system functions, its related latency requirements and validate the end-to-end latency at a high level. This functional architecture is then refined into an implementation deployed on an execution platform. As system description is evolving, the latency analysis is being refined with more precise values. Such an approach provide latency analysis from a high level specification without having to implement the system, saving potential re-engineering efforts. It also helps engineers to select appropriate execution platform components or change the deployment strategy of system functions to ensure that latency requirements will be met when implementing the system.This material is based upon work funded and supported by the Department of Defense under Contract No. FA8721-05-C-0003 with Carnegie Mellon University for the operation of the Software Engineering Institute, a federally funded research and development center

Ontology-based model-driven patterns for notification-oriented data-intensive enterprise information systems

International audienceIn the fourth industrial revolution, the current Enterprise Information Systems (EIS) are facing a set of new challenges raised by the applications of Cyber-Physical Systems (CPS) and Internet of Things (IoT). In this scenario, a data-intensive EIS involves networks of physical objects with sensing, data collection, transmission and actuation capabilities, and vast endpoints in the cloud, thereby offering large amounts of data. Such systems can be considered as a multidisciplinary complex system with strong interrelations between the involved components. In order to cope with the big heterogeneousness of those physical objects and their intrinsic information, the authors propose a notification-based approach derived from the so-called Notification Oriented Paradigm (NOP), a new rule and event driven approach for software and hardware specification and execution. However, the heterogeneity of those information and their interpretation relatively to an evolving context impose the definition of model-driven patterns based on some formal knowledge modelled by a set of skill-based ontologies. Thus, the paper focuses on the open issue related to the formalisation of such ontology-based patterns for their verification, ensuring the coherence of the whole set of data in each contextual engineering domain involved in the EIS

Considerations about Continuous Experimentation for Resource-Constrained Platforms in Self-Driving Vehicles

Autonomous vehicles are slowly becoming reality thanks to the efforts of many academic and industrial organizations. Due to the complexity of the software powering these systems and the dynamicity of the development processes, an architectural solution capable of supporting long-term evolution and maintenance is required. Continuous Experimentation (CE) is an already increasingly adopted practice in software-intensive web-based software systems to steadily improve them over time. CE allows organizations to steer the development efforts by basing decisions on data collected about the system in its field of application. Despite the advantages of Continuous Experimentation, this practice is only rarely adopted in cyber-physical systems and in the automotive domain. Reasons for this include the strict safety constraints and the computational capabilities needed from the target systems. In this work, a concept for using Continuous Experimentation for resource-constrained platforms like a self-driving vehicle is outlined.Comment: Copyright 2017 Springer. Paper submitted and accepted at the 11th European Conference on Software Architecture. 8 pages, 1 figure. Published in Lecture Notes in Computer Science vol 10475 (Springer), https://link.springer.com/chapter/10.1007/978-3-319-65831-5_

An Assurance Framework for Independent Co-assurance of Safety and Security

Integrated safety and security assurance for complex systems is difficult for many technical and socio-technical reasons such as mismatched processes, inadequate information, differing use of language and philosophies, etc.. Many co-assurance techniques rely on disregarding some of these challenges in order to present a unified methodology. Even with this simplification, no methodology has been widely adopted primarily because this approach is unrealistic when met with the complexity of real-world system development. This paper presents an alternate approach by providing a Safety-Security Assurance Framework (SSAF) based on a core set of assurance principles. This is done so that safety and security can be co-assured independently, as opposed to unified co-assurance which has been shown to have significant drawbacks. This also allows for separate processes and expertise from practitioners in each domain. With this structure, the focus is shifted from simplified unification to integration through exchanging the correct information at the right time using synchronisation activities

Mapping Big Data into Knowledge Space with Cognitive Cyber-Infrastructure

Big data research has attracted great attention in science, technology, industry and society. It is developing with the evolving scientific paradigm, the fourth industrial revolution, and the transformational innovation of technologies. However, its nature and fundamental challenge have not been recognized, and its own methodology has not been formed. This paper explores and answers the following questions: What is big data? What are the basic methods for representing, managing and analyzing big data? What is the relationship between big data and knowledge? Can we find a mapping from big data into knowledge space? What kind of infrastructure is required to support not only big data management and analysis but also knowledge discovery, sharing and management? What is the relationship between big data and science paradigm? What is the nature and fundamental challenge of big data computing? A multi-dimensional perspective is presented toward a methodology of big data computing.Comment: 59 page