On Service-Orientation for Automotive Software

Kugele, Stefan; Obergfell, Philipp; Broy, Manfred; Creighton, Oliver; Traub, Matthias; Hopfensitz, Wolfgang

doi:10.1109/icsa.2017.20

Cited by 34 publications

(9 citation statements)

References 11 publications

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“…The only exception is diagnostic tools which are five times more prominent among users (28) than organizations (5). Other than the above, we also notice a small number of automotive software repositories (32) relating to (driver) safety (like drowsiness detection) and security (tools for security testing or in-vehicle software for the security of the vehicle).…”

Section: Resultsmentioning

confidence: 99%

Painting the Landscape of Automotive Software in GitHub

Kochanthara¹,

Dajsuren²,

Cleophas³

et al. 2022

Preprint

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The automotive industry has transitioned from being an electromechanical to a software-intensive industry. A current high-end production vehicle contains 100 million+ lines of code surpassing modern airplanes, the Large Hadron Collider, the Android OS, and Facebook's front-end software, in code size by a huge margin. Today, software companies worldwide, including Apple, Google, Huawei, Baidu, and Sony are reportedly working to bring their vehicles to the road. This paper ventures into the automotive software landscape in open source, providing a first glimpse into this multi-disciplinary industry with a long history of closed source development. We paint the landscape of automotive software on GitHub by describing its characteristics and development styles.The landscape is defined by 15,000+ users contributing to ≈600 actively-developed automotive software projects created in a span of 12 years from 2010 until 2021. These projects range from vehicle dynamics-related software; firmware and drivers for sensors like LiDAR and camera; algorithms for perception and motion control; to complete operating systems integrating the above. Developments in the field are spearheaded by industry and academia alike, with one in three actively developed automotive software repositories owned by an organization. We observe shifts along multiple dimensions, including preferred language from MATLAB to Python and prevalence of perception and decision-related software over traditional automotive software. This study witnesses open source automotive software boom in its infancy with many implications for future research and practice.

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Section: Resultsmentioning

confidence: 99%

Painting the Landscape of Automotive Software in GitHub

Kochanthara¹,

Dajsuren²,

Cleophas³

et al. 2022

Preprint

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“…Besides these general contributions to formal service models applicable to embedded systems, automotive-specific work has been published. Recently, Kugele et al [54] introduced the idea of αSOA by formalising a framework for different service clusters that partly relies on the formal model of Broy and Stølen [17]. Afterwards, Cebotari and Kugele [20] refined this framework and described their transformation onto the modelling language Franca [27] used in the industry.…”

Section: Automotive Service-orientationmentioning

confidence: 99%

“…The architecture consists of several components, each providing or consuming data. Services and clients are structured in a layered architecture, as we proposed in [54]. The lowest layer consists of services that sense the environment with the help of sensors like lidar, radar, or cameras and influence the vehicle's movement with the help of actuators for steering and (de-)acceleration.…”

Section: Platform-independent Software Architecturementioning

confidence: 99%

Model-based resource analysis and synthesis of service-oriented automotive software architectures

2021

Self Cite

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Context Automotive software architectures describe distributed functionality by an interaction of software components. One drawback of today’s architectures is their strong integration into the onboard communication network based on predefined dependencies at design time. The idea is to reduce this rigid integration and technological dependencies. To this end, service-oriented architecture offers a suitable methodology since network communication is dynamically established at run-time. Aim We target to provide a methodology for analysing hardware resources and synthesising automotive service-oriented architectures based on platform-independent service models. Subsequently, we focus on transforming these models into a platform-specific architecture realisation process following AUTOSAR Adaptive. Approach For the platform-independent part, we apply the concepts of design space exploration and simulation to analyse and synthesise deployment configurations, i. e., mapping services to hardware resources at an early development stage. We refine these configurations to AUTOSAR Adaptive software architecture models representing the necessary input for a subsequent implementation process for the platform-specific part. Result We present deployment configurations that are optimal for the usage of a given set of computing resources currently under consideration for our next generation of E/E architecture. We also provide simulation results that demonstrate the ability of these configurations to meet the run time requirements. Both results helped us to decide whether a particular configuration can be implemented. As a possible software toolchain for this purpose, we finally provide a prototype. Conclusion The use of models and their analysis are proper means to get there, but the quality and speed of development must also be considered.

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“…However, as long as we have not reached a fully service-oriented architecture (SOA) (cf. e. g. [5]) on system level allowing to analyse and understand complex feature interactions in functional networks, we have to rely on messages and signals.…”

Section: Introductionmentioning

confidence: 99%