Architecture for embedded open software ecosystems
Artikel i vetenskaplig tidskrift, 2014

Software is prevalent in embedded products and may be critical for the success of the products, but manufacturers may view software as a necessary evil rather than as a key strategic opportunity and business differentiator. One of the reasons for this can be extensive supplier and subcontractor relationships and the cost, effort or unpredictability of the deliverables from the subcontractors are experienced as a major problem. The paper proposes open software ecosystem as an alternative approach to develop software for embedded systems, and elaborates on the necessary quality attributes of an embedded platform underlying such an ecosystem. The paper then defines a reference architecture consisting of 17 key decisions together with four architectural patterns, and provides the rationale why they are essential for an open software ecosystem platform for embedded systems in general and automotive systems in particular. The reference architecture is validated through a prototypical platform implementation in an industrial setting, providing a deeper understanding of how the architecture could be realised in the automotive domain. Four potential existing platforms, all targeted at the embedded domain (Android, OKL4, AUTOSAR and Robocop), are evaluated against the identified quality attributes to see how they could serve as a basis for an open software ecosystem platform with the conclusion that while none of them is a perfect fit they all have fundamental mechanisms necessary for an open software ecosystem approach.

Computer Science

COMPUTER SCIENCE

Software architecture

Theory &

Embedded software

ACTICE

Software ecosystem

CONSUMER ELECTRONICS

THEORY & METHODS

COMPUTER SCIENCE

TIME

Methods

P69

Computer Science

SOFTWARE ENGINEERING

Software Engineering

INDUSTRY

Författare

Ulrik Eklund

Malmo Hogskola

Volvo

Jan Bosch

Chalmers, Data- och informationsteknik, Software Engineering

Journal of Systems and Software

0164-1212 (ISSN)

Vol. 92 128-142

Ämneskategorier

Data- och informationsvetenskap

DOI

10.1016/j.jss.2014.01.009