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FIGURE A. Agile development of critical systems. The classic V model is evolving to a W model of continuous deliveries, starting at the bottom with design and integration, growing with service-oriented architectures, and moving upward to DevOps with over-the-air software updates to each vehicle. The LeanSAFE (Lean Scaled Agility for Engineering) Framework is Vector Consulting's implementation of the W model for critical systems.
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This theme issue addresses automotive IT and software development. What technologies and principles deliver value, and how can you introduce them at a fast pace?
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Context 1
... enhance the lifecycle toward agility and post- delivery continuous development. Using the concepts of DevOps and agility, change the classic V model to an agile cyclic W model (see Figure A). Focus on speed, syn- chronization, and integrity. Establish flexible synchronization points between hardware and software along the lifecycle to facilitate fast adaptation. Third, evolve embedded architectures toward the three- tier model: sensor and actor preprocessing, high-performance computing, and cloud services. Introduce service-oriented embedded architectures and adequate service delivery mod- els, such as predictive maintenance for better reliability and continuous delivery for flexibility. Enhance with the relevant design and testing ...Similar publications
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Citations
... Multiple technical domains are involved (e.g., entertainment or power train) that come with particular domain-specific issues (Weber and Weisbrod, 2002). Thousands of engineers collaborate in largescale distributed setups and need to fulfill a large variety of quality attributes (e.g., safety, performance, security, and usability) (Ebert and Favaro, 2017). The identified challenges raise the need to create novel development approaches and tools that allow practitioners to develop cost-efficient products in a highly complex domain . ...
... The variety of disciplines and the lack of common interdisciplinary understanding was found to be a complicating issue in automotive RE (Liebel et al., 2018). A rather rigorous RE approach is needed to create highquality products and support OEM-supplier relationships (Ebert and Favaro, 2017). ...
... While our findings are based on data that we collected within the automotive domain, we expect several of the findings to also be transferable to other large-scale systems and software engineering contexts. Due to the large variety of disciplines in automotive (Weber and Weisbrod, 2002;Ebert and Favaro, 2017;Broy et al., 2007), the heterogeneity of functions, and the supplier-OEM relationships, the need for diversity appears to be even more pronounced than in other industries. Future studies will examine the applicability of our findings and suggestions in other domains. ...
In large-scale automotive companies, various requirements engineering (RE) practices are used across teams. RE practices manifest in Requirements Information Models (RIM) that define what concepts and information should be captured for requirements. Collaboration of practitioners from different parts of an organization is required to define a suitable RIM that balances support for diverse practices in individual teams with the alignment needed for a shared view and team support on system level. There exists no guidance for this challenging task. This paper presents a mixed methods study to examine the role of RIMs in balancing alignment and diversity of RE practices in four automotive companies. Our analysis is based on data from systems engineering tools, 11 semi-structured interviews, and a survey to validate findings and suggestions. We found that balancing alignment and diversity of RE practices is important to consider when defining RIMs. We further investigated enablers for this balance and actions that practitioners take to achieve it. From these factors, we derived and evaluated recommendations for managing RIMs in practice that take into account the lifecycle of requirements and allow for diverse practices across sub-disciplines in early development, while enforcing alignment of requirements that are close to release.
... Multiple technical domains are involved (e.g., entertainment or power train) that come with particular domain-specific issues (Weber and Weisbrod, 2002). Thousands of engineers collaborate in largescale distributed setups and need to fulfill a large variety of quality attributes (e.g., safety, performance, security, and usability) (Ebert and Favaro, 2017). The identified challenges raise the need to create novel development approaches and tools that allow practitioners to develop cost-efficient products in a highly complex domain . ...
... The variety of disciplines and the lack of common interdisciplinary understanding was found to be a complicating issue in automotive RE (Liebel et al., 2018). A rather rigorous RE approach is needed to create highquality products and support OEM-supplier relationships (Ebert and Favaro, 2017). ...
... While our findings are based on data that we collected within the automotive domain, we expect several of the findings to also be transferable to other large-scale systems and software engineering contexts. Due to the large variety of disciplines in automotive (Weber and Weisbrod, 2002;Ebert and Favaro, 2017;Broy et al., 2007), the heterogeneity of functions, and the supplier-OEM relationships, the need for diversity appears to be even more pronounced than in other industries. Future studies will examine the applicability of our findings and suggestions in other domains. ...
... However, a number of challenges remain in adapting the processes of large organisations producing safety-critical systems to fulfil both the need for agility and the required rigour for certification. This is a particular problem in the automotive domain since the organisations involved in producing vehicles are very large, distributed over many disciplines and physical locations, and have established practices and tool-chains that are difficult to change [3,7]. This leads us to the following research questions: RQ1: Which common principles and practices can be derived from existing approaches for agile development of safety-critical systems? ...
Automotive companies increasingly adopt scaled agile methods to allow them to deal with their organisational and product complexity. Suitable methods are needed to ensure safety when developing automotive systems. On a small scale, R-Scrum and SafeScrum are two concrete suggestions for how to develop safety-critical systems using agile methods. However, for large-scale environments, existing frameworks like SAFe or LeSS do not support the development of safety-critical systems out of the box. We, therefore, aim to understand which challenges exist when developing safety-critical systems within large-scale agile industrial settings, in particular in the automotive domain. Based on an analysis of R-Scrum and SafeScrum, we conducted a focus group with three experts from industry to collect challenges in their daily work. We found challenges in the areas of living traceability, continuous compliance, and organisational flexibility. Among others, organisations struggle with defining a suitable traceability strategy, performing incremental safety analysis, and with integrating safety practices into their scaled way of working. Our results indicate a need to provide practical approaches to integrate safety work into large-scale agile development and point towards possible solutions, e.g., modular safety cases. Keywords: Scaled Agile, Safety-Critical Systems, Software Processes, R-Scrum, SafeScrum
... The automotive industry is facing several challenges ranging from the growing complexity of systems and software [13] to a costumers demand for a shorter time-to-market [34]. To overcome those "automated, connected, electrified and service" challenges the BMW Group set up a strategy [3]. ...
The growth in electrification and digitalization of vehicles leads to increasing variability and complexity of automotive systems. This poses new challenges for verification and validation, identified in a Product Line Engineering case study for the electric drive system. To overcome those challenges we developed a Product Line Testing methodology called TIGRE. In this paper, we present the TIGRE methodology. TIGRE comprises the identification and documentation of relevant data for efficient product line testing and the application of this data in the test management of an agile project environment. Furthermore, we present our experiences from the introduction into a large-scale industrial context. Based on our results from the introduction, we conclude that the TIGRE approach reduces the testing effort for automotive product lines significantly and, furthermore, allows us to transfer the results to untested products.
... In recent years, there has been an increased focus from industry and academia to investigate cloud platform architectures that enable continuous software delivery (CD) in vehicles [10]. Many industries have started to look for CD solutions as they need to release quality software more frequently, better respond to automotive market changes, avoid vehicle recalls, improve productivity, and increase customer satisfaction [28]. ...
The emerging usage of connected vehicles promises new business models and a high level of innovation, but also poses new challenges for the automotive domain and in particular for the connectivity dimension, i. e. the connection between vehicles and cloud environments including the architecture of such systems. Among other challenges, IoT Cloud platforms and their services have to scale with the number of vehicles on the road to provide functionality in a reliable way, especially when dealing with safety-related functions. Testing the scalability, functionality, and availability of IoT Cloud platform architectures for connected vehicles requires data from real world scenarios instead of hypothetical data sets to ensure both the proper functionality of distinct connected vehicle services and that the architecture scales with a varying number of vehicles. However, the closed and proprietary nature of current connected vehicle solutions aggravate the availability of both vehicle data and test environments to evaluate different architectures and cloud solutions. Thus, this paper introduces an approach for connecting the Eclipse SUMO traffic simulation with the open source connected vehicle ecosystem Eclipse Kuksa. More precisely, Eclipse SUMO is used to simulate traffic scenarios including microscopic properties like the position or emission. The generated data of each vehicle is then be sent to the message gateway of the Kuksa IoT Cloud platform and delegated to an according example service that consumes the data. In this way, not only the scalability of connected vehicle IoT architectures can be tested based on real world scenarios, but also the functionality of cloud services can be ensured by providing context-specific automotive data that goes beyond rudimentary or fake data-sets.
... In recent years, electronic control units (ECUs) and software have been developed in a distributed manner because functions, such as driving assistants and passenger convenience functions, have diversified [1]. The functions developed in this distributed environment are integrated into the system as a black box [2]. ...
Automotive electronic components are tested via hardware-in-the-loop (HiL) testing at the unit and integration test stages, according to ISO 26262. It is difficult to obtain debugging information from the HiL test because the simulator runs a black-box test automatically, depending on the scenario in the test script. At this time, debugging information can be obtained in HiL tests, using memory-updated information, without the source code or the debugging tool. However, this method does not know when the fault occurred, and it is difficult to select the starting point of debugging if the execution flow of the software is not known. In this paper, we propose a fault-localization method using a pattern in which each memory address is updated in the HiL test. Via a sequential pattern-mining algorithm in the memory-updated information of the transferred unit tests, memory-updated patterns are extracted, and the system learns using a convolutional neural network. Applying the learned pattern in the memory-updated information of the integration test can determine the fault point from the normal pattern. The point of departure from the normal pattern is highlighted as a fault-occurrence time, and updated addresses are presented as fault candidates. We applied the proposed method to an HiL test of an OSEK/VDX-based electronic control unit. Through fault-injection testing, we could find the cause of faults by checking the average memory address of 3.28%, and we could present the point of fault occurrence with an average accuracy of 80%.
... It comes with several challenges, as it involves the development of complex systems in collaboration with thousands of stakeholders from a plenitude of disciplines (mechanical engineering, software engineering, economics, electronics). Moreover, the automotive domain requires a variety of quality attributes to be fulfilled in distributed development environments with short cycle times 6 . It is a domain in which knowledge management 7 is of crucial importance, ...
http://regot.chalmers.se/wp-content/uploads/2019/04/2019_JSME_Wohlrab.pdf
Agile methods are increasingly introduced in automotive companies in the attempt to become more efficient and flexible in the system development. The adoption of agile practices influences communication between stakeholders and makes companies rethink the management of artifacts and documentation like requirements, safety compliance documents, and architecture models. Practitioners aim to reduce irrelevant documentation but face a lack of guidance to determine what artifacts are needed and how they should be managed. This paper presents artifacts, challenges, guidelines, and practices for the continuous management of systems engineering artifacts in automotive based on a theoretical and empirical understanding of the topic. In collaboration with 53 practitioners from six automotive companies, we conducted a design‐science study involving interviews, a questionnaire, focus groups, and practical data analysis of a systems engineering tool. The guidelines suggest the distinction between artifacts that are shared among different actors in a company (boundary objects) and those that are used within a team (locally relevant artifacts). We propose an analysis approach to identify boundary objects and three practices to manage systems engineering artifacts in industry.
... Complexity drivers in automotive[6]. ...
Testing is crucial to successfully engineering reliable automotive software. The manual derivation of test cases from ambiguous textual requirements is costly and error-prone. Model-based development can reduce the test case derivation effort by capturing requirements in structured models from which test cases can be generated with reduced effort. To facilitate the automated test case derivation at BMW, we conducted an anonymous survey among its testing practitioners and conceived a model-based improvement of the testing activities. The new model-based test case derivation extends BMW's SMArDT method with automated generation of tests, which addresses many of the practitioners' challenges uncovered through our study. This ultimately can facilitate quality assurance for automotive software.
... Based on benefits and success in software development, the agile paradigm has also been applied to systems engineering and product development contexts in several domains [13]. As it has become apparent that companies in competitive markets need to adopt cost-effective practices and flexibly react to change, the automotive industry also aims to improve agility and flexibility on system level [8]. However, besides the growing system complexity, the importance of safety, and a plethora of variants, also knowledge management has been reported a challenge in automotive software engineering [4,8]. ...
... As it has become apparent that companies in competitive markets need to adopt cost-effective practices and flexibly react to change, the automotive industry also aims to improve agility and flexibility on system level [8]. However, besides the growing system complexity, the importance of safety, and a plethora of variants, also knowledge management has been reported a challenge in automotive software engineering [4,8]. ...
Automotive companies increasingly include proven agile methods in their plan-driven system development. The adoption of agile methods impacts not only the way individuals collaborate, but also the management of artifacts like requirements, test cases, safety documentation, and models. While practitioners aim to reduce unnecessary documentation, there is a lack of guidance for automotive companies with respect to what artifacts are needed and how to manage them. To close this knowledge gap and create practical guidelines, we conducted a design-science study together with 53 practitioners from six automotive companies. Using interviews, surveys, and focus groups, we analyzed categories of artifacts and practical challenges to create applicable guidelines to collaboratively manage artifacts in agile automotive contexts. Our findings indicate that different practices are required to manage artifacts that are shared among different teams within the company (boundary objects) and those that are relevant within a specific team (locally relevant artifacts).
Recently, a data-driven approach has been widely used at various stages of the system development lifecycle thanks to its ability to extract knowledge from historical data. However, despite its superiority over other conventional approaches, e.g., approaches that are model-based and signal-based, the availability of representative datasets poses a major challenge. Therefore, for various engineering applications, new solutions to generate representative faulty data that reflect the real world operating conditions should be explored. In this study, a novel approach based on a hardware-in-the-loop (HIL) simulation and automated real-time fault injection (FI) method is proposed to generate, analyse and collect data samples in the presence of single and concurrent faults. The generated dataset is employed for the development of machine learning (ML)-assisted test strategies during the system verification and validation phases of the V-cycle development model. The developed framework can generate not only time series data but also a textual data including fault logs in an automated manner. As a case study, a high-fidelity simulation model of a gasoline engine system with a dynamic entire vehicle model is utilised to demonstrate the capabilities and benefits of the proposed framework. The results reveal the applicability of the proposed framework in simulating and capturing the system behaviour in the presence of faults occurring within the system’s components. Furthermore, the effectiveness of the proposed framework in analysing system behaviour and acquiring data during the validation phase of real-time systems under realistic operating conditions has been demonstrated.
