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DRAFT V.0.1

This page describes a simplified version of a methodology for designing artifacts that aim to solve specific technical problems. This abstract view of the methodology was inspired by the Design Science Research (DSR).*

* Disclaimer

The word "simplified" implies that the intention is not to replace or reinvent the Design Science Research (DSR) methodology already established in academic research. Instead, this interpretation adopts essential elements from DSR that can serve as a guideline to frame multiple collaborative projects within a unified specific structure. Please follow this simplified methodology for designing artifacts within the COVESA alliance. If you require a research-oriented design, please refer to the corresponding literature on DSR or similar.

DSR is used in fields like information systems and engineering to solve real-world problems by creating innovative solutions. Think of it as a way of applying a guideline (i.e., a structured process) to design something new or to answer open questions about an already existing design. In DSR, the innovative solution is generally referred to as an artifact. It's like an organized way of inventing cool stuff to solve practical problems!

Artifact types

The term "artifact" may sound too abstract and ambiguous at first. However, it is only a general way to cover multiple possibilities under the same methodology. In practice, this word is replaced with a specific artifact type. Nevertheless, using its generic form is helpful at the initial stages of a project, where the solution's particular shape has yet to be defined. As the list of specific artifacts could be extensive, one can directly link any piece of technology as a subcategory of these eight parent types (adapted from [*]):

Artifact typeExplanationExamples

System design

Description of a structure, a process, or some interaction.
  • Software architecture
  • Database schema
  • Business process diagram
MethodActivities that are performed by people in order to accomplish a particular task or solve a problem.
  • A/B testing method
  • Design thinking process
  • Kanban agile method
Language or NotationSymbols, rules, or conventions used to represent information or instructions as a formal abstraction of reality.
  • A data model of a particular domain (e.g., Vehicle Signal Specification)
  • A controlled vocabulary (e.g., a taxonomy of vehicle types)
  • An object-oriented programming
AlgorithmDescription of a machine-executable step-by-step procedure or set of rules designed to solve a problem or perform a specific task.
  • Sorting algorithm
  • Encryption algorithm 
  • Data mining algorithm 
GuidelineSuggestion regarding behaviour in a particular situation.
  • "If a vehicle data model has to be used in specific applications, use the parser to output the desired standard format and adapt the result to match the required schema."
RequirementsStatements about a system. They constraint the operational conditions.
  • "In a connected vehicle architecture, none safety-critical data points can be publicly shared."
  • "In a vehicle data architecture, the standards X must be supported."
PatternReusable elements of a design.
  • Asynchronous message queue
  • Service-oriented architecture
MetricAny model that can be used to evaluate aspects of system design.
  • Latency
  • Business case

Note:

This table was adapted from [1].


Fundamental components of an artefact design




References

[1]
A. Hevner, S. March, J. Park, and S. Ram, “Design science in information systems research,” MIS Quarterly, vol. 28, no. 1, pp. 75–105, 2004.

[2]
S. Gregor and A. Hevner, “Positioning and presenting design science research for maximum impact,” MIS quarterly, pp. 337–355, 2013.

[3]
R. J. Wieringa, Design Science Methodology for Information Systems and Software Engineering. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. doi: 10.1007/978-3-662-43839-8.

[4]
P. Offermann, S. Blom, M. Schönherr, and U. Bub, “Artifact Types in Information Systems Design Science – A Literature Review,”
in Global Perspectives on Design Science Research, vol. 6105, R. Winter, J. L. Zhao, and S. Aier, Eds., in Lecture Notes in Computer Science, vol. 6105. , Berlin, Heidelberg: Springer Berlin Heidelberg, 2010, pp. 77–92. doi: 10.1007/978-3-642-13335-0_6.

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