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Traceable Multi-view Model Integration: A Transformation Pipeline for Agile Production Systems Engineering


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Purpose Agile Production Systems Engineering (PSE) is characterised by parallel and iterative engineering of several disciplines. This multi-view engineering requires capabilities for tracing changes to support configuration management of PSE assets. Yet, traditional model transformation approaches in PSE do not preserve local views and hierarchies on concepts of PSE assets, such as plans and configurations. Thus, tracing multi-view changes to PSE assets is challenging. Method Following the Design Science approach, we (i) elicit requirements for tracing multi-view changes to PSE assets from a domain analysis in automotive manufacturing; (ii) introduce and evaluate the Traceable Multi-view Model Transformation (TMvMT) process; and (iii) propose the TMvMT pipeline architecture to provide traceable model integration capabilities for agile PSE. Results In a feasibility study on robot cell models, we evaluate the TMvMT process and architecture regarding the requirements for traceability compared to traditional approaches. Conclusion The proposed TMvMT approach provides traceability of changes in multi-view modelling as a basis through the separation of modelling transformation steps and provision of clear input and output artefacts to achieve traceable configuration management and validation of system designs for production system assets in agile PSE.
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SN Computer Science (2023) 4:205
SN Computer Science
Traceable Multi‑view Model Integration: ATransformation Pipeline
forAgile Production Systems Engineering
FelixRinker1,2 · LauraWaltersdorfer2· KristofMeixner1,2· DietmarWinkler1,2· ArndtLüder3,4· StefanBi2,4
Received: 1 November 2021 / Accepted: 16 December 2022 / Published online: 10 February 2023
© The Author(s) 2023
Purpose. Agile Production Systems Engineering (PSE) is characterised by parallel and iterative engineering of several dis-
ciplines. This multi-view engineering requires capabilities for tracing changes to support configuration management of PSE
assets. Yet, traditional model transformation approaches in PSE do not preserve local views and hierarchies on concepts of
PSE assets, such as plans and configurations. Thus, tracing multi-view changes to PSE assets is challenging.
Method. Following the Design Science approach, we (i) elicit requirements for tracing multi-view changes to PSE assets
from a domain analysis in automotive manufacturing; (ii) introduce and evaluate the Traceable Multi-view Model Trans-
formation (TMvMT) process; and (iii) propose the TMvMT pipeline architecture to provide traceable model integration
capabilities for agile PSE.
Results. In a feasibility study on robot cell models, we evaluate the TMvMT process and architecture regarding the require-
ments for traceability compared to traditional approaches.
Conclusion. The proposed TMvMT approach provides traceability of changes in multi-view modelling as a basis through
the separation of modelling transformation steps and provision of clear input and output artefacts to achieve traceable con-
figuration management and validation of system designs for production system assets in agile PSE.
Keywords Production systems engineering· Domain-specific modelling· Model-driven engineering· Domain-specific
languages· Model transformation· Multi-disciplinary engineering
Innovative Cyber-Physical Production Systems (CPPSs)
[1], envisioned in the Industry 4.0 (I4.0) Initiative1 [2], aim
at enabling sophisticated functionalities, such as predictive
maintenance or digital twins. However, many of these tech-
niques create large amounts of data, requiring new methods
to facilitate the reproducible management and integration of
engineering artefacts and data.
This article is part of the topical collection “Model-Driven
Engineering and Software Development” guest edited by Slimane
Hammoudi and Luis Ferreira Pires.
Laura Waltersdorfer, Kristof Meixner, Dietmar Winkler, Arndt
Lüder, Stefan Biffl have contributed equally to this work.
* Felix Rinker
Laura Waltersdorfer
Kristof Meixner
Dietmar Winkler
Arndt Lüder
Stefan Biffl
1 Christian-Doppler-Laboratory SQI, TU Wien, Vienna,
2 Institute ofInformation Systems Engineering, TU Wien,
Vienna, Austria
3 Institute ofErgonomics, Otto von Guericke University,
Magdeburg, Germany
4 Center forDigital Production, Vienna, Austria
1 Industry 4.0 Initiative: https:// www. platt form- i40. de.
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The artefacts describe views on PSE assets with
dependencies, e.g., joining products requires a correct level
of force depending on consistent contributions from the
mechanical, electrical, and software designs [3]. Consistent
PSE asset integration is needed to validate the functionality
and quality of modern CPPSs [1] and to reduce the risk of
project and production delay due to late design changes.
To manage multi-disciplinary PSE, the Association of
German Engineers published the VDI 3695 [4], a guideline
regularly used in the PSE context to describe conceptual
measures for engineering organizations. Particularly, VDI
3695 Part 2 (Processes) refers to an integral process called
Configuration Management (CM). This process constitutes
managing engineering work and its changes throughout the
plant lifecycle.
Similar to CM in Software Engineering, it should
enable a consistent and accurate documentation of various
characteristics, such as requirements, design, or testing
concerning the actual physical design [5]. However,
production systems have reached a high level of complexity
that makes conventional approaches to engineering
knowledge integration inadequate [3, 6]. Therefore, this
paper focuses on how to enable improved configuration
management and, thus, traceability on the level of data.
We aim to track changes of property values of PSE assets
and integrate discipline-specific engineering artefacts to
improve capabilities for agile PSE. A challenge in PSE
is that engineers typically conduct point-to-point artefact
exchange (cf. Fig.1), with only limited capabilities for
multi-view model versioning or change trace management.
Improving these capabilities requires a holistic view,
combining several views of engineering stakeholders and
project management, e.g., for quality management to test
different configurations of planned designs. Currently, there
is neither a holistic overview on nor a complete collection
of concepts used in a typical PSE project, e.g., for virtual
engineering [7], hampering traceability.
In practice, traceability is the capability to audit a
change’s origin to a common multi-view model element
by following the data flow back to an engineering artefact
in a particular stakeholder view. For instance, the value of
a property torque of a screwing process should be linked
to the relevant M-CAD tool data sheet (cf. Fig.3). In the
following, we will discuss several challenges to achieving
adequate traceability.
Challenges to Traceability in agile PSE. From the
industrial use case Position-and-Screw Robot Cell from
automotive manufacturing (cf. Sect.4.1 and literature [6,
8, 9]), we have identified several challenges. Figure1 illus-
trates how engineers conduct data and model exchange in
PSE across workgroup borders, in artefact-based transac-
tions [9]. Furthermore, the figure shows the challenges
marked with labels in red.
C1 Data artefact exchange instead of model exchange.
Custom file formats encode information on data
elements and hierarchies, in various forms, such as PDF,
spreadsheets, and technical drawings. Information is often
extracted manually and not integrated, after each update [8].
Fig. 1 Challenges in point-to-
point exchange of engineering
artefacts in agile PSE [10]Basic Planner
Automation Engineer
Electric Scr
Electric Screwdriver
Updates Backflow Engineering
Data Artifact
Data Exchange Data Delivery
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C2 Manual local model mapping and integration efforts.
Changes to multi-view models in PSE artefacts are hard to
trace using manual backtracking. For example, it is hard to
decide whether an input file is new data or an update of a
previous version, if tool data artefacts do not provide version
identifiers, e.g., in the file name. Model transformation and
integration require high effort due to custom data formats
and implicit model and mapping knowledge, which is
not formalised. These processes are not automated and
associated with high effort for the data consumers [6], which
can lead to error or loss of data.
C3. Missing common concept view. PSE tool suites are
tailored to selected engineering domains’ requirements
and hence fulfil use-case specific purposes for individual
disciplines [11]. However, such tool suites (e.g.: Creo2,
AutoCAD3 or EPLAN4) do not provide a holistic view
and data management beyond their limited scopes. For
example, there can be up to 15 disciplines and 50 tools in a
PSE project [12]. However, a partial overview is often only
achieved in later phases of the project with increased efforts
and when changes become more expensive and traceability
more complicated.
To overcome these challenges, we investigate how agile
software engineering approaches, such as model-based
software engineering [13] and continuous development
[14], have identified solution approaches to automate and
maintain software development processes. However, these
approaches assume only software artefacts and are not
sufficiently tailored to traceable multi-view model changes
in PSE [15].
Therefore, the main goal of this paper is to design and
evaluate an approach that facilitates traceable multi-view
model transformations in agile PSE. To address this goal,
we apply the Design Science approach, building on our
recent paper [7] with the following aims.
Aim 1. Traceability requirements. We define challenges
and requirements for tracing multi-view changes to
PSE concepts. Therefore, we build on the PSE guideline
VDI3695-2 for configuration management maturity [4]
and on the results of a domain analysis [16, 17] on work
cells in automotive manufacturing. This contribution targets
guiding the design of an approach for traceable multi-view
data modelling capabilities.
Aim 2. Traceable process. We introduce the Traceable
Multi-view Model Transformation (TMvMT) process with
knowledge representation in intermediate models for PSE
data integration. This process and knowledge representation
provide the foundation for implementing flexible model
transformation workflows for a defined scope of work. For
evaluating the viability in the scope of a manufacturing
work line, we conduct the process with selected changes to
engineering artefacts (a) to integrate the domain knowledge
scattered over several engineering views and (b) to verify the
traceability for changes to property values in the integrated
Aim 3. Software architecture. We refine the Multi-view
Model Transformation (MvMT) software architecture
building on the flexible pipeline software design [7],
to automate tasks in the TMvMT process and consider
traceability as a main goal of the workflow.
This work builds on and extends our previous work
on continuous model integration [7] and domain-
specific modelling [18]. In a first step, a more detailed
model transformation process is described, resulting in
an integrated engineering graph. Next, the Multi-view
Modelling Framework (MvMF) design towards traceable
model transformation is refined to automate asset property
value propagation. Furthermore, we extended the Product-
Process-Resource (PPR) Domain-Specific Language (DSL)
to define dependencies of requirements and integrated it into
the TMvMT. Finally, the model transformation process is
evaluated in comparison to alternative approaches.
In summary, our contributions in this paper to the
Computer Science (CS) community are as follows:
(i) We provide insights to CS researchers on PSE
domain concepts and traceability issues.
(ii) The TMvMT process to define traceable and flexible
multi-view model transformation pipelines for
building intermediate models and an integrated
PSE model, based on agile workflows to support
distributed modelling.
(iii) The paper explores the TMvMT architectural system
design for such a modelling transformation pipeline.
(iv) A feasibility study for the TMvMT approach
by providing a demonstrative use case and
implementation based on the PSE standard
AutomationML (AML) and automated with a
Continuous Integration (CI) system.
The remainder of this paper is structured as follows:
Section Related Work summarises related work.
SectionResearch Questions and Approach motivates the
research questions and approach. SectionEngineering Use
Case and Requirements presents an illustrative use case
on robot-based positioning and joining of car parts and
requirements for traceability. SectionTraceable Multi-view
Model Transformation describes (a) the Traceable Multi-
view Model Transformation (TMvMT) process to combine
stakeholder view models into an integrated engineering
model with capabilities for tracing changes to PSE assets
2 Creo: https:// www. ptc. com/ creo.
3 AutoCAD: https:// www. autod esk. com/ autoc ad.
4 EPLAN: https:// www. eplan. de.
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and (b) the TMvMT software architecture to automate
the TMvMT process, an improved model transformation
workflow based on continuous integration and model
engineering. SectionEvaluation with a Feasibility Study
demonstrates the feasibility of the TMvMT approach
with the illustrative use case Position-and-Screw Robot
Cell and compares the capabilities of the TMvMT
approach to alternative model transformation approaches
in PSE. SectionDiscussion discusses the research results
and limitations. Section Conclusion and Future Work
summarises the research findings and proposes future
Related Work
This section summarizes related work on modelling
interoperability, integrated production system modelling
for Industry 4.0, and model-based IT Operations (DevOps).
System Modelling
Modelling interoperability is an essential topic in (domain-
specific) system modelling, which has already gained
research attention over the last decades: Tolk and Muguira
[19] generalise conceptual interoperability and distinguish
five classes of models, from complete black-box applications
to white-box applications with harmonised data structures.
The Athena framework [20], a reference framework for
enterprise applications, considers three dimensions: (a)
conceptual, (b) applicative, and (c) technical integration. All
three layers are essential components for ensuring exchange
and integration of system models.
Model operations, especially model transformations, are
another essential measure to achieve the interoperability of
domain models. The Model-Driven Engineering (MDE)
community has established best-practice concepts and
frameworks: the ATLAS Transformation Language (ATL)5
framework conforms to the Meta Object Facility (MOF)6
specifications for model transformations and provides the
foundations for assuring modelling interoperability [21].
EMF Compare [22] is a framework for model comparison,
conflict detection and merging, mainly for technically
convergent application contexts.
In PSE, the technical divergence and multi-disciplinary
nature of the domain require the specification of explicit
dependencies and relations between different system
perspectives, based on implicit knowledge. Vogel-Heuser
etal. conceptually describe two approaches to overcome
inconsistencies, either a priori or a posteriori, based on
multiple use cases [3].
Both the modelling community and the PSE community
require adequate multi-view modelling processes and
frameworks [23, 24]. Atkinson etal. [25] suggest minimum
requirements for multi-level modelling, including: “some
fundamental notion of abstracting a multitude of model
elements to a common classifier”. Tunjic and Atkinson
[26] conceptualise a Single Underlying Model (SUM) as
a common unified model, which could be automatically
populated based on the information from the single views.
Therefore, the authors describe a modelling approach
to preserve local views by defining mappings and
representations between local and common views.
To summarise, there are many established approaches
from model engineering ranging from the generation of
software code to model comparison, which are beneficial
for automation and quality assurance purposes. However,
these approaches do not offer extended domain-specific
capabilities for the PSE context. To fill this gap, evolving
initiatives in industrial PSE increasingly have proposed
designs, which we will discuss in the following subsection.
Integrated System Modelling forIndustry 4.0
PSE aims to create a consistent set of engineering models
required to physically set up the intended production
system [4]. According to the guideline VDI3695 [4],
engineering organizations might expose different levels
of quality related to their technical background, business
processes, and capabilities. These levels of target statuses
range from A (lowest maturity) to D (highest maturity)
and have descriptions which features characterise them. In
the following, we will refer to specific maturity levels and
describe how to achieve them for relevant topics.
Model Architecture Types
In industrial PSE practice, we can observe three main
architecture approaches establishing the technical
background of model transformation in engineering
organizations: Manual Model Transformation (MMT), Tool
Suite with input/output Model Transformation (TS-MT), and
Multi-view Model Transformation (MvMT).
MMT concerns the manual transfer of engineering data/
models between engineering tools [12, 27]. This approach
requires only capabilities for exchanging data files and
organizational rules to coordinate the data exchange.
Proper timing of exchange in the PSE process and sufficient
model transformation capabilities are essential. Benefits
are low effort for setup and operation for simple settings.
However, support for traceability is limited the risk and
effort increase more than linear for larger, more complex
5 ATL: https:// www. eclip se. org/ atl.
6 MOF: https:// www. omg. org/ mof.
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settings. According to the guideline VDI 3695 [4], MMT
provides, at best, sparse integration between the data sets
of two disciplines.
The more advanced approach TS-MT is based on tool
suites (e.g., Siemens COMOS7) supporting the integrated
engineering of a subset of the relevant PSE disciplines
[11]. TS-MT offers much support for dedicated use cases.
However, TS-MT requires high effort for the initial setup as
well as for the adaption to new views and lacks flexibility
beyond dedicated use cases. According to the guideline
VDI 3695 [4], TS-MT partly integrates some disciplines
very well, while disciplines outside the selected scope of
the tool suite are not well integrated and, in general, hard
to integrate.
MvMT covers all relevant PSE disciplines, however,
without dedicated tracing capabilities. MvMT systems are
based on a common semantic understanding of all data
relevant within an engineering organization and enable
automated PSE data propagation along the complete
engineering tool chain of the PSE organization [7, 28,
29]. Considering the requirements of the VDI 3695 [4]
for PSE projects, MvMT fully integrates disciplines with
reasonable incremental effort. This characteristic leads to
a common model for a sufficiently complete scope of PSE
stakeholders. However, change propagation with MvMT
does not necessarily imply the traceability of changes.
In this paper, we build on these traditional approaches
for model transformation to evaluate traceability capabilities
(cf. Sect.6.3).
Domain‑Specific Modelling Technologies
In PSE, DSLs are essential for facilitating model-based
engineering in specialised domains and implementing the
Industry 4.0 vision for complex data-driven use cases, such
as smart production [30]. The VDI3695-3 [4] guideline
on description languages (DL) specifically focuses on
this aspect. Level A (lowest maturity)requires the usage
of structured description languages. To achieve level
VDI3695DL-D (highest maturity), additionally identical
facts have to be always described equally and the mapping
of languages to semantics has to be consistent.
However, PSE processes are often optimised for intra-
disciplinary activities lacking the common view perspective
[31]. This issue limits the effectiveness and efficiency of
interdisciplinary knowledge exchange and favours domain-
specific approaches. Expressing the dependencies within
production systems requires comprehensive models as the
different disciplines and their concepts are inherently linked
In software engineering, efforts have been made to
introduce code pipelines that automate building and
deployment processes [33] and could be adapted to the PSE
Several initiatives have developed industry standards that
have been increasingly used to model and specify domain-
specific contexts: For instance, modelling languages are
Systems Modelling Language (SysML)8 and AutomationML
(AML)9 for engineering data exchange, the Business
Process Model and Notation (BPMN)10 for generic process
description in use cases, or Simulink11 for control and
signal processing. However, data exchange in PSE settings
still concerns the exchange of documents [34] with highly
heterogeneous data formats, hindering seamless model
integration and transformation. Integration on multiple
levels is required to reap the benefits of digitization in the
manufacturing domain: Hildebrandt etal. [35] showcase
meta-models described in AML and with domain-specific
ECLASS12 attributes for a mechatronic model.
The BaSys 4.0 project13 investigated a run-time
middleware for Industry 4.0 (I4.0) components [36] and
assumes the modelling of stakeholder views as I4.0 Asset
Administration Shell (AAS) I4.0 Asset Administration Shell
(AAS) [37], a digital representation of the I4.0 component.
However, the BaSys 4.0 project did not consider how to
derive the required I4.0 ASS specifications from multi-view
engineering artefacts.
Schleipen etal. [38] introduced a common integrated
model to PSE, the PPR model, which is based on the three
main aspects of a production system. These are the product
with its properties, the process that produces this product,
and the resource that executes production processes. The
Formalised Process Description (FPD) [39] represents
these aspects in a technology-agnostic way by defining a
graphical notation and a data model for the functional view
on a production system. However, the FPD does not provide
functionality to formulate consistency constraints, like the
maximal weight of a set of production resources.
Meixner etal. [18] introduced the PPR-DSL, a machine-
readable and technology-agnostic DSL for PSE modelling,
to represent PPR aspects and constraints between these
aspects. Furthermore, they developed a mapping of the
constraint syntax to recursive Structured Query Language
(SQL) queries [40] to execute them on relational database
systems, a well-established technology in PSE. In this paper,
7 https:// www. sieme ns. com/ comos.
8 SysML: https:// www. sysml. org.
9 AutomationML: https:// www. autom ation ml. org.
10 BPMN: https:// www. bpmn. org.
11 Simulink: https:// www. mathw orks. com/ simul ink.
12 ECLASS: https:// www. eclass. eu.
13 BaSys 4.0: https:// www. basys 40. de/.
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we build on the PPR-DSL [18] to express the functional view
in PSE. We extend it to describe dependencies for enabling
traceability of value changes across discipline-specific
views. Early in PSE, this view models the requirements
towards a production system (cf. Sect.4).
To conclude, comprehensive and systematic integration
of all relevant system aspects is of high importance to
enable innovative use cases around Industry 4.0 and CPPSs.
For this reason, we build in this paper on established
modelling frameworks, such as EMF Compare and
theMOFspecification, with domain-specific multi-view
capabilities [7]. Current approaches focus on the integration
of run-time processes, while our work focuses on integrating
model in the engineering phase. To ensure traceability
during the engineering phase, we investigate solution
approaches from software engineering, such as continuous
integration, regarding their suitability for the PSE context.
Model‑based DevOps forTraceability
Traceability is an essential feature for multi-disciplinary PSE
[41, 42] approaches. To achieve traceability, collaborating
engineering work groups need to agree on boundary objects
[43]. These boundary objects act as links for tracing across
gaps between different disciplines and provide essential
foundations for configuration management. Guideline
VDI 3695-2 [4] concerns configuration management
in PSE and defines four maturity levelsfromA (lowest
maturity) to D (highest maturity). The overall aim is to move
towards coordinated configuration management for multi-
view models, instead of basic isolated discipline-specific
configuration. These multi-view models should incorporate
all relevant disciplines and require reference models for each
discipline and the connection across disciplines to achieve
maturity level CM-D in the VDI3695-2 [4]. Such examples
might include mechanical and electrical plans, software
code, and respective configurations for the automation and
simulation purposes (cf. Sect.4).
In software engineering, DevOps [14] focuses on enabling
continuous integration to achieve, among other goals,
traceability: agile approaches, such as the Git workflow14,
and orchestration and automation software (e.g., Docker15,
Ansible16, Chef17) improve the quality and traceability of
software development processes. Furthermore, configuration
management in continuous integration environments is
based on configuration files (often text) that provide details
about infrastructure and parameters to trace different
versions. Both approaches, model-based engineering and
the DevOps movement, have established methodologies
and tools for supporting traceable workflows and increasing
software quality. However, until now, these approaches
have been focused on code-related, text-based artefacts
and not on models Garcia etal. [33] extend the concept of
continuous integration and present a model-based DevOps
approach with continuous development tools. Wortmann
etal. [30] survey the state of art in modelling languages
in industrial contexts. As one contribution, they propose a
vision of combining the model-based approach with DevOps
technologies to support Industry 4.0 developments and point
out relevant research directions. In this paper, we build on
the traceability concepts in the guideline VDI3695-2 [4] and
the model-based DevOps vision in [30] to design a traceable
multi-view model transformation approach.
In this paper, we extend our previous work [7] by the
use case Position-and-Screw Robot Cell (cf. Sect.4). From
this use case, we design an integrated engineering view
represented in the PPR-DSL [18]. The resulting multi-view
engineering graph is the foundation (i) to model a SUM
and (ii) to specify model transformation configurations that
provide an agile model transformation pipeline (cf. Sect.5).
Research Questions andApproach
In this paper, we follow the Design Science methodology
[44] to address the main research question asking what
approach facilitates traceable multi-view model transfor-
mations in agile PSE. To this end, we investigate how to
improve the representation and transformation of multi-
disciplinary knowledge for tracing changes to PSE assets in
engineering artefacts, achieving VDI3695-2 maturity level
CM-D [4] (cf. Sect.2). Figure2 shows the Design Science
methodology for this work instantiated as research steps and
Step 1, Environment Analysis, concerned the investigation
of the results from a domain analysis [16] (cf. Sect.4). This
domain analysis was conducted on 80 types of robot-based
screwing processes in the automotive industry, which is
representative for discrete manufacturing for a product
portfolio with high variability. The investigation resulted in
(i) an abstracted description of the stakeholders, their tasks,
views, typical engineering artefacts, and data integration
concerns. Based on the use case (ii), requirements were
derived on multi-disciplinary knowledge representation
and integration for PSE change management and traceability
from changed engineering artefacts to an integrated data
Step 2, Design/Build (cf. Fig.2), derived the following
research questions.
RQ1. Process Design. What process enables traceable
multi-view model transformation workflows in agile PSE?
14 Git Workflow: https:// www. git- scm. com/ about/ distr ibuted.
15 https:// www. docker. com.
16 https:// www. ansib le. com.
17 www. chef. io.
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Domain experts exchange heterogeneous data in multiple
iterations and horizontally across disciplines. This context
can lead to changes coming from any of these disciplines.
Domain experts require traceable transformation workflows for
avoiding inconsistencies, errors, or data silos. To address RQ1,
we designed the TMvMT process (cf. Sect.5.1) with a focus
on traceability (i) to define a multi-view engineering graph,
(ii) to configure a multi-view modelling environment, and (iii)
to execute the data integration pipeline for propagating shared
data, provided from engineering artefacts, to an integrated
model and to data consumers (cf. Fig.7).
RQ2. Architecture Design. What software architecture
enables a traceable model transformation workflow in agile
PSE? Efficient model transformation requires an architecture
that is compatible with typical PSE system landscapes. To
address RQ2, we designed the TMvMT software architecture
(cf. Sect.5.2) to automate major parts of the TMvMT process.
We extend our previous architecture design [7] (i) with
capabilities to describe multi-view engineering graphs and (ii)
with extended rule engine functionalities to enable attribute
value traceability.
Step 3, Justify/Evaluate (cf. Fig.2), aims at demonstrating
the feasibility of the TMvMT approach with the illustrative use
case Position-and-Screw Robot Cell (cf. Sect.4). We analyse
the TMvMT process results, e.g., integration of a local view to
a common view PSE data model, for improving the traceability
of changes. Further, we aim at better understanding benefits
and limitations of the TMvMT approach in comparison to
traditional approaches. Therefore, we conducted the TMvMT
process to instantiate a TMvMT pipeline. We evaluated
to what extent the TMvMT process and prototype fulfil the
requirements for traceability and the effort for conducting
the TMvMT process. We compared the results to traditional
alternative approaches in PSE: (i) Manual Transformation
between engineering artefacts, without a common view [12]
(cf. Sect.4); (ii) a Tool Suite with a limited common view [11];
and (iii) our previous work of the MvMT [7] with a common
unified view, but without traceability concerns.
Engineering Use Case andRequirements
This section describes the illustrative use case Position-and-
Screw Robot Cell from automotive manufacturing [16], and
requirements for traceable multi-view model transformation
Position‑and‑Screw Robot Cell
The use case Position-and-Screw Robot Cell is based on a
domain analysis [16] from discrete automotive manufacturing,
i.e., the production of car parts and cars. The production lines
used in automotive manufacturing utilise industrial robots in
mounting units, e.g., to position and fasten screws. A typical car
production plant holds around 200 to 300 of such robot cells.
For illustrative purposes, we assume a scenario with two
robot cells, one for Positioning and one for Screwing the
required car screws (cf. Fig.3). The purpose of this production
process is to mount a dashboard to a car body. The process
consists of two steps: The first robot cell carries out the correct
positioning of the dashboard and the screws in the car body.
Then, the second robot cell fastens the screws and measures the
result. A major challenge in planning the production process
is integrating and coordinating the different discipline-specific
engineering artefacts, which are iteratively affected by updates
of PSE design decision outcomes.
Stakeholders and their views. Figure3 shows selected
stakeholders and their partial views on the overall production
system design. The PSE process starts with functional
Fig. 2 Research steps, methods,
and contributions (in IDEF0
notation [45])
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SN Computer Science (2023) 4:205205 Page 8 of 25
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system planning, followed by detailed mechanical and
automation engineering.
For conciseness, Fig.3 shows the Quality Engineer as a
proxy for the combined views of the Product Designer and
the Functional Planner. The Product Designer is responsible
for the design of the product, such as a car part, and has to
consider customer and technical requirements. Functional
Planners take up the product design and requirements from
the previous step and develop a conceptual production
system design to produce the product variants required
by the customer. Therefore, they define the input and
output products together with the production system and
quality attributes, such as cycle time and screwing torque.
Additionally, they specify the main CPPS resources, which
are required for the production processes.
In the detailed design phase, detail engineers select
and integrate concrete system components to ensure the
feasibility of the conceptual production system design.
While real-world engineering scenarios involve 15+
different views, we focus on detailed mechanical, electrical,
and automation engineering.
Detailed mechanical engineering concerns concrete
mechanical system parameters and arguments, adding
mechanical characteristics to processes and resources.
For example, the Mechanical Engineer would add the
mechanical property torque to the abstract electric
Common concepts concern, e.g., the property torque
that is assigned to the process and related to the torque of
the electric screwdriver. The values of such an attributes
can be propagated to the other attribute. In Fig.3, these
dependencies are represented as orange relations.
Furthermore, CPPS subresources, such as two drives,
provide subfunctions for the main CPPS resources.
Detailed electrical engineering concerns the wiring
to supply energy and information to production system
resources. For instance, the Electrical Engineer specifies
CPPS subresources, such as the transformer, the robot
controller, and the screwdriver controller. The electric layout
also defines network details regarding high and low power
supply and the fieldbus network.
Detailed automation engineering builds on the
aforementioned artefacts to design configurations and
programs that automate the behaviour of the production
system. Examples for such artefacts are, e.g., available
resources, conceptual process design, and input from
mechanical/electrical engineering. Many of the technical
details for system components usually stem from existing
in-house technologies or third-party vendor catalogues.
The columns in Fig.3 categorise common concepts
based on the PPR notation [39]: Products & Processes and
CPPS Resources, detailed as Main CPPS Resources, CPPS
SubResources, and Automation Resources. Furthermore,
Plant Networks provide information and electrical power
supply, and topological information. Engineering Artefacts
represent stakeholder documents, which contain actual
engineering data values according to stakeholder views.
Requirements forTraceable Multi‑view Model
From the domain analysis of the use case [16] and the
VDI3695-2 [4] maturity status level CM-D for configuration
management for PSE, we derived the following requirements
(Rx) for traceable and agile model transformation workflows.
R1. Multi-view modelling capabilities. The PSE process
needs to support multiple stakeholder views and their
artefacts (cf. Fig.3). Therefore, stakeholders should be able
to define local concepts in discipline-specific design views
and models that can be mapped to common concepts. The
transformation workflow shall support and preserve these
multiple stakeholder views regarding engineering artefacts
and the integrated model as a foundation for tracing back
model changes.
R2. Distributed process synchronization. The
engineering disciplines have to synchronise and discuss
changes to designs to gain a common view of updated
information. This synchronization capability is the
foundation to check for inconsistencies (a) in the common
view, e.g., inconsistent changes to several values that
depend on each other, or (b) between stakeholder views,
i.e., inconsistent values of one common concept in
different stakeholder views. The transformation workflow
shall provide capabilities for synchronising distributed
engineering processes sufficiently in the parallel and iterative
development of different production system parts.
R3. Traceable model change representation.
Heterogeneous artefacts are common in PSE: Discipline-
specific concepts and representation formats describe
different views. The transformation workflow shall consider:
(i) a common view that bridges these concerns and provides
a comprehensive understanding of dependencies and links
of the system model; (ii) the representation of change
dependencies; (iii) the capability to trace back changes
from the integrated model to its sources, such as a change
to a model element, e.g., a property value in a local model
view, which can be directly mapped or semantically linked
to the integrated model; and (iv) a description language for
defining a common view and the dependencies.
R4. Version representation/management. The traceable
model transformation shall represent and facilitate managing
versions of engineering models and artefacts as required for
parallel and iterative collaboration of several engineering
disciplines. For example, the VDI3695-2 guideline [4]
requires capabilities for resetting a model to a historical
state if changes lead to an invalid production system
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Process Resource
Product Process-Resource
Products &
Plant Network
Fasten Screw
& Measure
Car Body with
Car Body with
screwed on
Position Screw
& Dashboard
Car BodyScrew
Fasten Screw
& Measure
Position Screw
& Dashboard
Screwdriver Drive3
Robot Controller
Transformer High Power
Low Power
Proxy for
Planner Robot Controller
Bill of
Fasten Screw
& Measure
Data Sheet
Data Sheet
Data Sheet
Property-Property Dependency Reference
Property-Artifact Source Reference
Property-Property Value Propagation Reference
Fig. 3 Use case Position-and-Screw Robot Cell: stakeholder views, concepts, and artefacts
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SN Computer Science (2023) 4:205205 Page 10 of 25
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configuration. The transformation workflow shall provide
capabilities for version representation/management to
conduct configuration management in PSE.
Traceable Multi‑view Model Transformation
To address the requirements for traceable model
transformation workflows (cf. Sect.4.2), we propose in
this section (i) the TMvMT process to combine stakeholder
view models into an integrated engineering model and to
configure a multi-view modelling environment and (ii) the
TMvMT software architecture to automate the TMvMT
process in a multi-view modelling environment.
Traceable Multi‑view Model Transformation Process
The collaborative and parallel nature of PSE requires a
common understanding and agreement on boundary objects
[43], e.g., building on an integrated engineering graph.
Furthermore, traceability in the parallel working environment
requires flexible model and data exchange. To achieve such a
flexible multi-view data integration and exchange, we build
on the Multi-view Model Transformation (MvMT) workflow
[7], which is explained in the following subsection.
Multi‑view Model Transformation Workflow
Multi-view model transformation requires the synchronization
of multiple disciplines and collaborative workflows. These
advanced capabilities are required to address more complex
goals, such as traceability or documentation, e.g., for digital
twins, predictive maintenance, or retrofitting tasks.
Established process models for such collaborative
approaches are, for instance, defined workflows for source
code management. Inspired by the agile development move-
ment, Git supports an agile distributed non-linear workflow,
initially developed for software engineering. However, while
Git is well suited for text-based change detection and tracing,
it lacks capabilities for advanced analysis on a semantic level,
which is required for tracing model changes (cf. also [22]).
The MvMT workflow [7] is based on the SUM architecture
[26] and on the Git workflow. In our case, the SUM is
represented by a unified view model that explains how
different discipline-specific views and overlapping model
components are mapped into a common view. For illustrative
purposes, Fig.4 depicts the interaction of two stakeholder
views with the unified view model, consisting of the tasks: (1)
integration of View Model A into the unified view model (cf.
label v2 in Fig.4); (2) integration of View Model B into the
unified view model (cf. label v3 in Fig.4); and (3) export of
the modified View Model A from the unified view model (cf.
label x1 in Fig.4). While engineers work on their particular
local views, changes that concern the common view are
incorporated into the unified view model.
However, conducting the MvMT workflow requires first
the definition of a unified view model (cf. Fig.4) considering
all relevant views. Therefore, additional steps are required to
facilitate tracing changes back to the local engineering views.
These TMvMT process steps are described in the following
Traceable Multi‑view Model Transformation Method
To address RQ1 (cf. Sect.3), Fig.5 illustrates the TMvMT
process that consists of the following three steps to prepare
inputs for MvMT pipelines: (1) the definition of a multi-view
engineering graph as a foundation for (2) the configuration of
the multi-view modelling environment; and (3) the execution
of the data integration pipeline.
Step 1: Definition of multi-view engineering graph. To
achieve traceability and a holistic overview of engineering
artefacts, an integrated engineering view is required (cf.
Fig.9). Therefore, a specialised domain expert, the data
curator, guides the engineers of the different disciplines in
the process of defining a multi-view engineering graph. This
graph provides a common understanding of concepts, their
relations between each others, mappings between different
concepts, and dependencies. The definition can be separated
into the following abstraction levels:
Concepts. Domain experts collect local concepts of their
domains from relevant engineering artefacts in their particular
Unified View Model
View Model A
View Model B
Fig. 4 Agile multi-disciplinary artefact synchronization based on the SUM [26] and on Git workflow concepts
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Local View. Examples are in the Quality View the required
torque (cf. Fig.6 top-left) and in the Mechanical View the
torque (cf. Fig.6 top-right).
Common Concepts. The engineers map the local
concepts to Common Concepts (CCs) following the CCG
approach [31] depicted in Fig.6. For instance, for the
process Fasten Screw & Measure, the domain-specific
attributes torque in the view of the quality engineer and the
mechanical view are mapped in the Common View to the
process Fasten Screw & Measure attribute torque (cf. Figs.3
and6). The result is a list of mappings between the concepts
of different domain-specific views and the common view.
Links. There are two main purposes for links: (i) They
specify from which engineering artefacts (e.g., documents)
values originate, and (ii) change propagation of dependent
values, if the source element of an element is changed, the
target element should also be changed. The data curator,
with the support from the domain experts links, e.g., the
local mechanical torque attribute to the corresponding
mechanical artefact source such as a M-CAD drawing
(cf. Fig.3). For this task, a linking language is needed
to represent semantic dependencies. Therefore, for our
purposes, we will utilise a semantic linking language based
on the RefSemantic concept [46] following the URI schema
Fig. 5 TMvMT process steps
(in IDEF0 notation [45])
Domain Specific Concepts
Common Concepts
Common View
Local View
Local Concept Dissemination
Domain Specific Concepts
Local View
Quality View
Position Screw & Dasboard
Fasten Screw & Measure
- cycle_time
- cycle_time
- req_torque
- tension
Mechanic View
Position Screw & Dasboard
Fasten Screw & Measure
- pos_accuracy
- torque
Electric Screwdriver
- torque
Automation View
Screwdriver Controller
Robot Controller
- screw_curve
Fasten Screw &
Fasten Screw &
Position Screw
& Dashboard
Electric View
Electric Screwdriver
Screwdriver Controller
- power_consumption
- power_consumption
Fig. 6 Exemplary mapping of selected local concepts in stakeholder views (cf. Fig.3) to a common view, based on the Common Concept Glos-
sary (CCG) approach [31]
18 RFC3986: https:// datat racker. ietf. org/ doc/ html/ rfc39 86.
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Engineering Graph Template. Conceputal planners,
responsible for the plant floor design, create a first pro-
duction system design that initiates the initial engineering
graph. Therefore, they use the previously defined CCs by
categorising them according to the PPR aspects: product,
process, and resource.
Updated Engineering Graph. This initial design is
then filled in and updated by the other domain experts,
who specify open and/or updated domain-specific attribute
values. This information is required to reflect sources and
interdependencies between different discipline model
elements and attributes in the graph. The multi-view
engineering graph resulting from this step is the basis to
generate the unified and discipline-specific view models.
Step 2: Configuration of the multi-view model
transformation environment.
The setup of an executable TMvMT environment requires
(i) a unified view model, acting as SUM; (ii) discipline-
specific view models; and (iii) data integration workflow
descriptions between the disciplines.
The basis for the unified view model is the engineering
graph created in Step 1. However, the graph needs to be
adapted to be used in the data integration workflow. The
project generator of the MvMT framework supports the
generation of the unified view model based on the updated
engineering graph. The discipline-specific view models will
be also generated with the generator from the engineering
graph. These templates provide the scope for the view-
specific local concepts needed to describe the discipline-
specific data points. For the date integration workflow
descriptions, (i) process descriptions of the data flow
required as well as, (ii) data and model capabilities, such as
text-to-model and model-to-model operations. Furthermore,
the data curator designs model data exchange flows between
the discipline-specific data sources. To automate the data
exchange flows, the data curator creates transformer
definitions, describing the mapping of the discipline-specific
model concepts to the concept in the unified model.
Step 3: Execution of multi-view model transformation
Based on the multi-view model transformation configu-
ration coming from Step 2, the transformation pipeline is
executed to perform the required model transformation work-
flows. Figure7 depicts an exemplary TMvMT workflow that
consists of three steps, Step 3.1 to Step 3.3.
Step 3.1 Integration of the Quality View Model. The Quality
Engineer starts the model transformation workflow by editing
an artefact in a discipline-specific tool, Tool A, (cf. Fig.7, upper
left-hand side). The engineer wants to integrate the modelling
information into the unified view model (cf. Fig.7, Common
View lane). First, the artefact is exported from the discipline-
specific Tool A into an export format, e.g., a Extensible Markup
Language (XML) or Comma Separated Value (CSV) file. Then, a
transformer transforms the Tool-A-specific format into the Quality
View Model, respectively, the previously defined discipline-
specific template. This populated template is then compared to
the SUM (cf. Fig.7, Common View lane) to detect differences
between the two versions based on the changes. Changes can
include new elements and the modification of elements, e.g.,
the change of a property value. The result of this step is a list of
changes, which can be reviewed by the Quality Engineer. A core
advantage of this task is to allow the engineer to specify which
changes to accept or decline. Based on this finalised list, the
changes are merged into the unified model, creating a new version.
The changes are then available for all stakeholders of the workflow
when accessing the new unified model version (cf. Figs.4 and 7).
Step 3.2 Integration of the Mechanical View Model. The
tool-specific data of a discipline-specific tool, Tool B, have
to be transformed into the discipline-specific Mechanical
View Model using the corresponding template. Then, the
transformed structure is compared to the new unified model
version. Different to Step 3.1, this unified model version
View Model
Compare Diff Model
View Model Compare Diff Model
Workflow Vi ew Model Projection
View Model
Fig. 7 Model transformation workflow for combining tool artefacts in an SUM [26] and the Git workflow. (1) Integration of the Quality View
Model, (2) Integration of the Mechanical View Model, and (3) Export of the modified the Quality View Model, based on [7]
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incorporates the previous changes to the Quality View
Model. Similar to Step 3.1, the changes are calculated
resulting in a list of changes. The Mechanical Engineer can
select or reject changes and merge the model data to the
unified model version creating an updated model.
Step 3.3 Extraction of the Modified Quality View Model.
Based on the unified model version, created in Step 3.2, the
Quality Engineer can extract the most recent unified model
version, which incorporates the changes coming from both
the Quality Engineer and the Mechanical Engineer. The
local view of the Quality View Model can be generated from
this updated unified model version, enabling the Quality
Engineer to access the data in her discipline-specific Tool A.
Conducting these TMvMT workflows requires the
following capabilities in an architecture: (i) the capturing
and support of the distributed working process; and (ii)
advanced model comparison approaches that extend text-
based diffs to model-based change analysis.
Traceable Multi‑view Model Transformation
To automate TMvMT process, this section describes an
architectural system design for a TMvMT pipeline for PSE.
Figure8 shows the proposed architecture of the TMvMT
pipeline system that contains, from left to right, three main
components: (i) the PPR Modelling Framework (PPRMF),
(ii) the Multi-view Modelling Framework (MvMF), and (iii)
the Model Service Command Line Interfaces (CLIs) and
(iv) the Model Transformation Pipeline (MTP) component.
In [7], we developed the MvMF to provide the multi-
view modelling work process capabilities. The MvMF is
motivated by the Eclipse Modelling Framework (EMF),
a meta-modelling framework that offers comparing and
merging functionality [47] for integrating heterogeneous
models. However, EMF is tightly coupled with Eclipse19
and has complex interdependencies. These issues make the
EMF hard to use for users without model-driven software
engineering expertise or set up the EMF in custom software
solutions [48]. This shortcoming is a major drawback in the
PSE application context.
From the domain analysis (cf. Sect.4) and previous work
[6], we learned that accessibility and understandability of
modelling and model integration processes are major con-
cerns for engineers. Approaches in industry, such as low code,
have been devised [49, 50] to reduce setup and configuration
effort for domain experts, who are not familiar with software
engineering or model engineering techniques. Therefore,
we applied the principles from the EMF to our use case by
designing a light-weight Service-Oriented Architecture (SOA)
to enable model engineering and transformation in PSE with
little setup and configuration effort. In the following, we will
explain the different components in more detail:
The PPR Modelling Framework (PPRMF) (cf. Fig.8)
provides the PPR-DSL and the Project Configuration
Generator. The PPR-DSL is an external component that
supports the modelling of PPR networks by providing a text-
based definition language as well as parsing and validation
features [18]. To enable traceability, we extended the PPR-
DSL model and the prototypical DSL framework with
semantic linking features. This extension allows to describe
property value sources, dependencies, and propagation of
attribute values to other properties (cf. Sect.6 and Fig.10).
We newly introduced the concept of Relations. There are
three types of Relations: (i) Source Relations, specifying the
origin of a value, (ii) Dependency Relations, that provide a
linking between the source value and the dependent value,
and (iii) Propagation Relations, values in one concept that
need to be propagated to other concepts. Relations can
have the following attributes: type (source, dependency, or
propagation), semantic (reference key for the engineering
artefact), and reference (attribute name in the engineering
artefact or target attribute).
The Project Configuration Generator uses the specified PPR
network and generates a project configuration. This configura-
tion consists of common concepts, partial discipline-specific
Comparator Pipeline Configurations
Converter XML
Multi-view Modelling
Model Transformation
Model Generator
Logical Unit /
Model Integration Model Testing
Model Service CLIs
Automation /
CI Server
PPR Modelling
Fig. 8 Architectural system design of the TMvMT Pipeline System and its components
19 Eclipse: https:// www. eclip se. org.
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views and attributes, and relations between attributes to source
elements. Furthermore, it contains an initial graph network to
generate the unified view and local view templates. This con-
figuration subsequently serves as an input for the Project Gen-
erator (cf. Sect.6 and Fig.11).
The Multi-view Modelling Framework (MvMF) uses the
SUM approach as a metamodel according to Layer 2 of the MOF
architecture to preserve local views while providing mappings
to a unified view. The framework implements model operations
including: Model-to-Text and Model-to-Model transformation,
model Comparison and Merging, model Injection, and model
Validation. The MvMF includes four components:
Model Generator. The Project Generator (cf. Fig.8)
constructs the SUM template and corresponding discipline-
specific views required for the MvMF for a particular
project. For this reason, discipline-specific knowledge
and hierarchies stemming from custom tools must be
externalised and encoded in a common computer-readable
format (e.g., YAML20), the project generator configuration.
Model Integration. The Model Integration components
(cf. Fig.8) support the multi-view model integration
workflow and consist of four model operation services: The
Converter restructures a view-specific model into a SUM
compliant-structure to provide the required input for the
Comparator in the next step. In addition, the service can also
retrieve a view-specific model from the SUM, if required
by an engineer. The Comparator derives the delta model
by comparing two models and calculating the differences.
The Merger merges changes from the comparator into the
SUM. The RuleEngine enables traceability between element
mappings. Semantic links between elements (cf. Fig.10)
provide the foundation for automated change propagation.
For instance, if the torque value in the mechanical view is
semantically equal to the torque value in the quality view,
a change in one view will be propagated to the other view.
Transformer. The CSVTransformer and
XMLTransformer provide Text-to-Model transformation to
import and export the tool-specific artefacts. The transformer
has to be configured for an engineering artefact using a
custom object mapping language (cf. Fig.12).
Model Testing. UnitTests check the consistency and
quality, e.g., of the model data. In the development phase,
these tests can check new configuration workflows [51].
The Model Service Command Line Interfaces (CLIs)
component facilitates access to MvMF services via CLIs. This
CLI service can be used to define a workflow by combining
several services in a shell script. Furthermore, this enables a
flexible configuration of MvMF services in DevOps automation
tasks, like build pipelines on a continuous integration server.
The Model Transformation Pipeline provides means
for workflow definition descriptions. The different MvMF
services can be orchestrated through these pipeline
configuration using a domain-specific language. New
pipelines can easily be defined and deployed, by providing
an additional pipeline configuration, e.g., generation of a
report based on SUM for management.
Evaluation withaFeasibility Study
This section demonstrates the feasibility of the Traceable
Multi-view Model Transformation (TMvMT) approach with the
illustrative use case Position-and-Screw Robot Cell (cf. Sect.4).
We conducted the TMvMT process (cf. Sect.5.1) to instantiate
a traceable model transformation pipeline and an integrated
model for the use case. Based on results of the feasibility study,
we evaluate the TMvMT approach regarding requirements
for traceability in comparison to three traditional alternative
approaches (cf. Sect.2.2): (i) Manual Model Transformation
between engineering artefacts, without a Single Underlying
Model (SUM) (cf. Sect.4); (ii) a Tool Suite with a limited SUM;
and (iii) Multi-view Model Transformation with an SUM, but
without traceability concerns.
Evaluation Context
Based on the use case Position-and-Screw Robot Cell (cf.
Sect.4), Fig.9 illustrates the combined view of the separate
stakeholder views for the process Fasten Screw & Meas-
ure shown in Fig.3, as a result of conducting the TMvMT
process. We will refer to Electric Screwdriver as one exam-
ple of a common concept, integrating the mechanical and
the electrical view through semantic links. The mechanical
property torque is extracted from M-CAD engineering arte-
facts. The electric property power consumption is extracted
from E-CAD engineering artefacts. We will showcase the
traceability functionality through the two types of property
linkings: source reference, from where a value originates
from, dependency reference and propagation reference to
change a dependent value based on a source value.
Feasibility Study
This section investigates the feasibility of the TMvMT approach by
instantiating a model transformation pipeline of the use case. Full
versions of the discussed excerpts of the multi-view engineering
graph and the configurations, as well as as set of input and output
files of an example pipeline execution can be accessed on this online
repository21. Binaries of the the prototype that are used to execute the
pipeline can be found in the Bin folder.
20 YAML: https:// yaml. org/.
21 TMvMT Resources Repository: https:// github. com/ tuw- qse/
tmvmt- resou rces.
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Step 1 of the TMvMT process (cf. Sect.5.1) defines the
multi-view engineering graph.
Figure 9 shows the conceptual view of such an
engineering graph. To achieve traceability, the data curator
needs to add relationships between concept attributes to the
graph. As described previously, there are three different
types of such relations: blue lines indicate Dependency;
orange lines Value Propagation and Source References.
Figure10 shows an excerpt of a common concept Electric
Screwdriver in the engineering graph defined in the PPR-
DSL (cf. screw-dashboard.dsl22, depicting different views
and the source artefact. Attribute names (Line 4) are pre-
fixed by the view name to organise them later on into differ-
ent views in Step 2. For example, the attribute mechanical.
torque has two relations (lines 4-15). The first relation of
type source describes a reference to another attribute in the
Process hierarchy . In this case, the element is located in an
XML document, and can be retrieved using an XQuery23
term, defined in the reference property. The propagate refer-
ence describes how an attribute refers to another attribute in
the engineering graph, which can be automatically updated
during the change process. Similarly, a second attribute
power_consumption is defined for the electrical view (lines
Step 2 configures the model transformation workflow
environment. Therefore, first, the PPR engineering graph
needs to be translated by the Project Configuration Genera-
tor (cf. Table1) into the project configuration. A project
configuration defines the discipline-specific view models to
guide the transformation of local concepts into correspond-
ing concepts in the SUM template. Figure11 shows a sim-
plified example project configuration, specified in a simple
structured markup language, Yet Another Markup Language
The discipline-specific view models are specified in lines 5-8
and transform the local concepts into concept descriptions in
the SUM definition. Our example concept ElectricScrewdriver
is specified (line 12) and the concept mappings according to the
model views (PPR, mechanical, electrical) are defined (lines
14, 16, and 31). Attributes are put under the respective view,
based on the view prefix from Step 1. In our case, the attribute
torque with prefix mechanical in the engineering graph
(Fig.10, line 4) matches the defined mechanical-view attribute
torque in the project configuration (Fig.11, line 18). Each view
provides further tool-specific attributes. As a result, the SUM
configuration contains all relevant views, concepts, attributes,
and reference links in a list (cf. generator-config.yml24). The
AML Project Generator (cf. Table1) is used to build the AML
SUM model and local view models templates25).
Fasten Screw
& Measure
Car Body with
Robot Controller
M.Pos_accuracy E.Power_cons
Car Body with
screwed on
Property-Property Dependency Reference
Process Resource
Product Process-Resource
High Power
Low Power
Products &
Plant Network
Property-Artifact Source Reference
Property-Property Value Propagation Reference
Fig. 9 Multi-view engineering graph of the process Fasten Screw & Measure based on the the use case Position and Screw: with change depend-
ency trace links, in an adapted VDI3682 notation, based on [17]
22 screw-dashboard.dsl: https:// raw. githu buser conte nt. com/ tuw- qse/
tmvmt- resou rces/ main/ uc- screw_ dashb oard/ gener ate/ proje ct- config-
pprdsl/ input/ screw- dashb oard. dsl.
23 XQuery: https:// www. w3. org/ XML/ Query.
24 generator-config.yml: https:// raw. githu buser conte nt. com/ tuw- qse/
tmvmt- resou rces/ main/ uc- screw_ dashb oard/ gener ate/ proje ct- config-
pprdsl/ output/ gener ator- config. yml.
25 Generated Model Templates: https:// github. com/ tuw- qse/ tmvmt-
resou rces/ tree/ main/ uc- screw_ dashb oard/ gener ate/ proje ct/ output.
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As a next task, the data curator has to define data trans-
formation workflows. These discipline-specific workflows
either deliver view-specific data to the SUM or export view-
specific data from the SUM. Figure12 shows a simplified
example of a transformer configuration, in the modelling lan-
guage YAML as an example (cf. mechanical-import-config.
yml26. The AML Transformer (cf. Table1) represents trans-
former implementations for AML and for Text-to-Model and
Model-to-Text transformations. For Text-to-Model imports,
the data artefact usually is an export from a discipline-spe-
cific tool. The data curator defines object mappings (line
3) between the particular data artefacts and the local view
model. Line 4 indicates the expression for which element
type in an XML documents the mapping is applicable (cf.
mechanical-import-data.xml27). Specifically, these mappings
describe which artefact elements map to which AML con-
cept, in the example a particular systemUnitClassPath (line
5). Conditions can further detail the source element (line
8), specifying that the XML element attribute @DescrEN=
needs to have the value ElectricScrewdriver.
To further prepare the workflow, the data curator has
to set up a pipeline configuration file. Figure13 shows an
excerpt of the pipeline configuration using the domain-
specific definition language of the Jenkins server with
several steps. Additionally, the workflow can be defined as
shell scripts.
An workflow setup with the input and output artefacts is
available in the online repository28. First, each view-specific
transformer is defined, which takes as input the respective
view template, transformer configuration, input data, and
the file path to the output file. The outputs are the particu-
lar view templates populated with the data values from the
input data that come from the engineering artefacts. Then,
the view model is converted, and inputs to this step are the
local view model and the SUM.
Then, the local view model is translated into the SUM
structure using the CAEX Converter (cf. Table1). The
CAEX Comparer (cf. Table1) compares this SUM-struc-
tured view model with the contents of the SUM resulting
in a diff-model that contains the computed changes of the
comparison (cf. mechanical-view-compare-result.json29).
Figure14 show an excerpt of the diff-model that contain-
ing the detected changed value of the attribute torque .
Also, a new link element is detected that describes the
hierarchy dependency between the ElectricScrewdriver
and the Bit. Engineers can investigate the changes in the
diff-model and accept or reject them (cf. line 4). For com-
plex changes that affect several disciplines, an additional
multi-view change management approach can be applied
[52]. Based on their input, the CAEX Merger processes the
diff-model and applies the changes to the SUM instance.
The CAEX RuleEngine evaluates the semantic links to
propagate the changes across the local view models defined
in the project configuration. The current implementation
of the TMvMT approach focuses on tracing new model
elements and attribute value modifications, as these are the
most frequent sources of change.
Step 3 executes the multi-view model transformation
workflow. The data curator uploads the project configuration,
defined in Step 1, to the Jenkins server. Data updates in a
shared data repository trigger the execution of the associated
view pipelines. The data curator can inspect partial results,
such as the input data, configuration data, converted view
models, and the diff model, to validate the correct execution
of the pipeline.
Figure15 shows the defined stages of the pipeline, con-
sisting of a tool installation and general setup of the pipeline
environment. After this task, the project’s model transform-
ers are initially generated, and three view transformations
for the different disciplines are executed.
First, the required modelling operation services (provided
as jar-files) are defined in the tools section. In consecutive
stages, the model transformations for the different views are
executed with their specific configurations. The data curator
can easily modify the pipeline steps in the Continuous
Integration server and review the implications. Jenkins
executes the model transformations providing feedback on
every step’s success (or failure) and writes the resulting
models to the respective locations. The feedback can further
be visualised in an issue tracker or reporting system.
Furthermore, another advantage compared to the manual
model transformation process is the multi-view modelling
environment. In the manual process, the addition of views
can lead to breaking workflows or errors due to the point-
to-point update flows. According to the TMvMT process,
new views are incorporated to the multi-view engineering
graph (cf. Sect.5.1, Step 1). The next step guarantees that
the semantic links and dependencies are generated. Based
on the feasibility study, we evaluate the capabilities of the
TMvMT approach in the following section.
Implementation and modelling technologies. Table1
shows the mappings between modelling concepts and imple-
mentation technologies in the feasibility study. Command
line versions of the implementations used in the feasibility
26 mechanical-import-config.yml: https:// raw. githu buser conte nt. com/
tuw- qse/ tmvmt- resou rces/ main/ uc- screw_ dashb oard/ trans form/ input/
mecha nical- import- config. yml.
27 mechanical-import-data.xml: https:// raw. githu buser conte nt. com/
tuw- qse/ tmvmt- resou rces/ main/ uc- screw_ dashb oard/ trans form/ input/
mecha nical- import- data. xml.
28 https:// github. com/ tuw- qse/ tmvmt- resou rces/ tree/ main/ uc- screw_
dashb oard.
29 mechanical-view_compare-result.json: https:// raw. githu buser conte
nt. com/ tuw- qse/ tmvmt- resou rces/ main/ uc- screw_ dashb oard/ integ rate/
compa re/ output/ mecha nical- view_ compa re- result. json.
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SN Computer Science (2023) 4:205 Page 17 of 25 205
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study can be found in the online repository30. The imple-
mentation extends the MvMF [7], using the industrial engi-
neering data exchange standard AutomationML (AML)
[53] to define the SUM. Furthermore, we use and extend
the PPR-DSL [18], to model the concrete data model of the
multi-view engineering graph. The Model-to-Model trans-
formation is conducted by the CAEX Converter, which con-
verts view models into the SUM structure to enable model
comparison. The conversion can also transform the SUM
Fig. 10 RefSemantic in the
attributes of the resource elec-
tric screwdriver
Resource"ElectricScrewdriver": {
20 }
Fig. 11 Project definition for a
position-and-screw robot cell
- concept: "ElectricScrewdriver"
32 ...
30 TMvMT Implementation: https:// github. com/ tuw- qse/ tmvmt- resou
rces/ tree/ main/ bin.
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SN Computer Science (2023) 4:205205 Page 18 of 25
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structure back into a specific view model. In this case, the
view-specific data are extracted from the SUM.
The CAEX Comparer implementation for model
comparison is based on the internal hierarchical structure of
AML files, i.e., the Computer-Aided Engineering eXchange
(CAEX) structure. Our CAEX Comparer, similar to EMF
Compare, computes the comparison and diff analysis of the
model based on element attribute content rather than on a
textual representation. The service compares the converted
view model in the SUM structure to the currently instantiated
SUM and generates a list of model differences. This delta
list can be reviewed to either accept or reject single changes.
The CAEX Merger merges the reviewed list of changes into
the SUM to generate an updated version. After the merge,
CAEX Rule Engine propagates changes based on defined
rules. This can happen if model elements or attributes
have references, which indicate semantic similarity, to
view updates. Subsequently, unit tests on different stages
conduct model validation. Automating the improved model
transformation workflow requires a flexible method to link
the transformations to a pipeline sequence. For this purpose,
we chose for our prototypical implementation Jenkins as
an automation/CI server to combine the engineering with
DevOps and execute the model transformation workflow.
Evaluation ofTraceability Capabilities
This section evaluates the fulfilment of the traceability
requirements (cf. Sect.4), expected extra effort, and
complexity for establishing traceability in PSE.
Evaluation of traceability requirements. We evaluate
the TMvMT approach regarding requirements for trace-
ability and effort with traditional alternative approaches in
PSE (cf. model architecture types in Sect.2.2): (i) Manual
Model Transformation (MMT) between engineering arte-
facts, without a Single Underlying Model (SUM) [12] (cf.
Sect.4); (ii) a Tool Suite with a limited SUM (TS-MT) [11];
and (iii) Multi-view Model Transformation with a SUM, but
without specific traceability concerns [7]. In the evaluation,
we illustrate these alternative approaches with application
examples from PSE practice, e.g., concrete implementations
of the approaches in commercial or custom tools at a PSE
A main advantage of a model transformation pipeline
as described in [7] compared to the other approaches is
the separation of concerns. For example, understanding
local model elements or mapping and transforming local
to common concepts lead to a higher number of simpler
transformers when compared to the TS-MT approach.
Therefore, these transformers are easier to reuse and require
only limited knowledge for their adaptation, allowing the
data curator to share the work load with local domain
experts, e.g., an electrical domain expert. However, change
propagation with MvMT does not necessarily imply the
traceability of changes.
Traceable Multi-view Model Transformation (TMvMT)
in PSE (cf. Sect.5) extends the MvMT approach with a
traceability concern: Changes to model elements, in
particular to property values, are traced back to the
sources of change in a discipline. This is needed to act
as a foundation for auditable and verifiable configuration
management in parallel and iterative PSE as required to
realise the Industry 4.0 vision.
Table2 summarises the fulfilment of the requirements
Rx, introduced in Sect.4, on a 5-point Likert scale (
, +,
o, -,
), where
indicate very high/low capabilities
for the TMvMT approach and alternative transformation
workflow approaches (cf. Sect.2.2).
R1. Multi-view modelling capabilities. MMT is rated
very low due to point-to-point transformation without an
integrated model. TS-MT is rated high as transformers map
stakeholder views in engineering artefacts to an integrated
model. MvMT is rated very high as the approach explicitly
represents stakeholder views both in the engineering
artefacts and in the integrated model. TMvMT is rated very
high as the approach explicitly represents and preserves
stakeholder views both in the engineering artefacts and in
the integrated model.
R2. Distributed process synchronization. MMT is rated
very low due to very limited synchronization capabilities
via document-based exchange, triggered manually by
domain experts, insufficient representation of configuration
dependencies as mainly tacit domain expert knowledge,
Fig. 12 Transformer configura-
tion for the electric screwdriver
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SN Computer Science (2023) 4:205 Page 19 of 25 205
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and no systematic process support for parallel and iterative
development. TS-MT is rated average as the tool suite can
act on changes for a limited set of engineering disciplines.
However, it does not consider dependencies between data
elements, as discipline-specific views are not represented
in the integrated model. These issues puts the burden of
identifying relevant changes on the user or on hard-coded
scripts. Dependencies are hard-coded in importer scripts
making the propagation of changes inflexible and hard
to adapt for a domain expert. MvMT is rated high as the
representation of change views per discipline and change
dependencies provides the foundation for automated change
propagation and flexible analysis and adaptation. TMvMT
is rated very high as it goes beyond the MvMT approach by
representing dependencies and states for each asset element
as a foundation for synchronising agile PSE processes.
R3. Traceable model change representation. MMT is
rated very low due to the missing common model, possibly
incompatible description languages of the engineering
artefacts, and the missing representation of change
dependencies. These features make the capability to trace
back a change to its source depend on the knowledge of
the involved domain experts rather than on a systematic
approach. TS-MT is rated low as the description languages
are compatible only for a limited scope of stakeholder
views that are very hard to extend. Further, the collection
of changes to model elements that may come from several
disciplines is very difficult to trace back to the respective
source. It requires to analyse log files, which are not visible
to the normal user, taking high effort for the data curator.
MvMT is rated average as the integrated model can represent
the full scope of stakeholder views and change dependencies.
However, it does not consider version numbers and considers
only stakeholder roles but no individuals. The latter may
submit conflicting changes that are hard and error-prone to
trace back to individual stakeholders within a discipline.
TMvMT is rated high as it goes beyond the MvMT approach
by considering change states for asset elements and both
stakeholder roles and individuals.
R4. Version representation/management. MMT is
rated very low due to typically an event/timestamp-based
sequence of changes that provide only fragile version man-
agement capabilities. TS-MT is rated average as version
management is possible for model elements, but only in lim-
ited scope of stakeholder views that is very hard to extend.
MvMT is rated average as the approach does not consider
version numbers of asset elements. TMvMT is rated high as
it goes beyond the MvMT approach by supporting version
numbers for internal elements and concepts.
Effort for model transformation. PSE domain experts
will only consider changing their approach to model
transformation, if the effort not too high. The baseline
with which new approaches are compared is the traditional
approach, in this case manual model transformation.
Therefore, we compare the expected effort for the model
transformation alternatives. MMT is rated high due to
the very low effort for setup, taking into account the high
incremental effort for operation, often leading to infrequent
model updates and technical debt [6]. TS-MT is rated
average due to the high effort required for the first setup of
the common data model and system architecture. Further,
the extension of the tool suite with new stakeholder views is
very costly, requiring the involvement and approval of tool
suite consultants. MvMT is rated high as the first setup of the
common data model and system architecture requires high
effort. During operation effort is reduced, and the reuse of
artefacts, methods, and configurations facilitates the efficient
inclusion of new stakeholder views and engineering tools.
TMvMT is rated high as it has similar effort characteristics
as the MvMT approach. Considering traceability in the
TMvMT is a one-time cost, as it is incurred while designing
the multi-view engineering graph.
Traceability Factors. In the feasibility study context, we
identified as factors that are likely to influence the quality of
model integration and traceability effectiveness and effort in
agile PSE: (i) the scope of the model, e.g., one work cell or
several work cells in a potentially large work line; (ii) the
Fig. 13 Jenkins pipeline con-
figuration for pipeline stages
2tools {
3jdk 'openjdk11'
5stage('Project Generation'){
6steps {
7configFileProvider([configFile(fileId: '115b',
8targetLocation: '${GEN_IN}/aml-gen-config.yml')]) {}
9sh 'java -jar aml-class-gen.jar -c
-t ${GEN_IN}/usedLibs.aml -o ${GEN_OUT}'
stage('Next stage'){steps { ... }}
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SN Computer Science (2023) 4:205205 Page 20 of 25
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number and complexity of stakeholder views that have to be
integrated, e.g., disciplines working in parallel, engineer-
ing artefact types, and tool export formats; and (iii) input
data quality, reflecting technical debt [6] input data that may
accumulate in the course of a project and across projects due
to reuse of data models and instances. While these factors
Fig. 14 Example comparison
result of the resource electric
screwdriver in the mechanical
view to the SUM
2"type" :"AttributeModify",
3"changeKind" :"CHANGE",
4"accepted" :false,
5"leftParent" :{
6"type" :"Attribute",
7"id" :null,
8"name" :"torque",
9"parentElement" :{
"type" :"Element",
"id" :"e1d6dd5a-6024-4977-9591-bbd8bad87a33",
"name" :"MechanicalView"
"rightParent" :{
"type" :"Attribute",
"id" :null,
"name" :"torque",
"parentElement" :{
"type" :"Element",
"id" :"e1d6dd5a-6024-4977-9591-bbd8bad87a33",
"name" :"MechanicalView"
"property" :{
"type" :"Property",
"name" :"value"
"oldValue" :"0.0",
"newValue" :"5"
}, {
"type" :"ElementChange",
"changeKind" :"ADD",
"accepted" :false,
"leftParent" :{
"type" :"Element",
"id" :"e1d6dd5a-6024-4977-9591-bbd8bad87a33",
"name" :"MechanicalView"
"rightParent" :{
"type" :"Element",
"id" :"e1d6dd5a-6024-4977-9591-bbd8bad87a33",
"name" :"MechanicalView"
"value" :{
"type" :"Link",
"id" :"10a562f5-4a1d-449a-b50e-3caa2c442b4b",
"name" :"ElectricScrewdriver.MechanicalView:MechanicalView_toChild-
"refPartnerSideA" :"e1d6dd5a-6024-4977-9591-bbd8bad87a33:MechanicalView_toChild"
"refPartnerSideB" :
53 }
Fig. 15 Jenkins Build Pipeline
with six steps, based on [7]
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had a limited impact in the feasibility study, they should be
considered when applying the TMvMT approach in larger
This section discusses the research results and limitations
regarding the research questions introduced in Sect.3. In
our previous work [7], we presented the MvMT workflow
as an improved model transformation method compared to
manual model integration. The presented approach is based
on the Git workflow and multi-view modelling. The aim
is to facilitate the traceable mapping and incorporation of
different view models to a SUM. The continuation of this
work was now applied to the Position and Screw use case
and we extended the PPR-DSL implementation to define and
represent the three different relation types.
RQ1. Process Design. What process enables traceable
multi-view model transformation workflows in agile PSE?
To address RQ1 and the requirements for traceability (cf.
Sect.4), we proposed in Sect.5.1 the TMvMT process.
This process aims to extend the MvMT method to enable
the design and operation of a customisable and traceable
multi-view model transformation workflow.
The TMvMT process consists of three steps: (1) Define
a multi-view engineering graph to achieve a holistic and
common view on engineering concepts. This is achieved
through negotiating common concepts, defining semantic
references to describe the origin of attribute values and map-
pings within the network. (2) Configure multi-view model-
ling workflows to setup a flexible environment. Traceability
is enabled through the automatic generation of the SUM
and local view models derived from the engineering graph.
(3) Execute data integration pipeline. In this step, the dis-
tributed model operations—compare, diff, and merge—ease
the model integration process and facilitate reviewing and
tracing partial results. For example, input and output models
can be viewed to validate mapping results. Coupled with
the model transformation pipeline, these model transforma-
tion services can be flexibly orchestrated and automatically
Based on the feasibility study regarding the traceability
requirements, the comparison of the TMvMT results showed
clear improvements over the traditional alternative model
transformation approaches (cf. Table2), in particular
regarding traceable model change representation, version
representation, and distributed process synchronization. The
study results indicate that the TMvMT approach provides a
sound foundation for PSE domain experts to define multi-
view models in a traceable way. This foundation gives
way for an evaluation of the approach in a broader context
regarding its usability and scalability various PSE scenarios
of different size and complexity.
RQ2. Architecture Design. What software architecture
enables a traceable model transformation workflow in agile
PSE? A major goal of Development and IT Operations
(DevOps) and MDE architectures is to increase productivity
through automation and orchestration of processes. For
Model-Driven Engineering (MDE), this includes the
automated generation of models and code, while DevOps
focuses on automated integration and testing. Although
the Eclipse Modelling Framework (EMF) provides rich
functionality for MDE, it would have introduced too much
complexity to our context. For this reason, we decided to
reuse our custom-built Multi-view Modelling Framework
(MvMF) [7], incorporating the main MDE principles, while
keeping overall configuration effort lower.
To address RQ2, we extended the MvMF [7] by designing
a TMvMT software architecture to automate the TMvMT
process and to facilitate tracing changes to attribute values.
The TMvMT process requires a modelling capability to
design the engineering graph. Domain-Specific Languages
(DSLs) are established means to provide such capability for
domain-specific contexts such as PSE. A well-established
modelling concept in the PSE domain is PPR; thus, we
decided to reuse PPR-DSL [18]. In the feasibility study,
we showed how to automate the TMvMT workflow, using
defined build pipelines in Jenkins. The project definition can
be reused for other projects or adapted to changing needs.
PSE engineers also benefit from the pipeline configuration,
which allows an adaptation to different contexts, i.e., use
cases in the multi-disciplinary PSE domain.
Table 1 TMvMT process step–
modelling concept–technology
TMvMT Process Modelling Concept Technology
Step 1 Modelling Framework MvMF, PPR-DSL
Step 2 Meta-Model Design ProjConfigGen, AML ProjGen
Step 3 Text2M2Text Transformation AML Transformer
Step 3 M2M Transformation CAEX Converter
Step 3 Model Comparison CAEX Comparer
Step 3 Model Merge CAEX Merger
Step 3 Model Injection CAEX RuleEngine
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Our research results go beyond the state of the art in
model-based software engineering by showcasing an indus-
trial use case from the PSE domain and the feasibility of a
domain-specific model-based DevOps approach.
On the other hand, our research results exceed the state
of the art in model integration for PSE: (i) by addressing
the integration of multi-view stakeholder models in PSE;
(ii) by focusing on model-based analyses of changes, rather
than text-based analyses that do not work well in a PSE
context with heterogeneous engineering artefacts; (iii) by
providing the modular, configurable TMvMT approach,
building on the EMF concept with different technologies
that are suitable for an application in the PSE context; (iv)
by providing the PSE research community with a modular
Continuous Integration (CI) approach that works with
a variety of artefact types; and (v) by demonstrating the
feasibility of conducting the TMvMT process to integrate
a multi-view model for a typical PSE scope of a robot work
cell in automotive production.
Limitations. The feasibility study focused on a use case
derived from projects at large PSE companies in automotive
industry. This may introduce bias due to the specific
selection of stakeholder views and alternative approaches
considered, as well as the roles or individual preferences of
the domain experts. To overcome these limitations, we plan
case studies in a wider variety of application contexts.
The current implementation aims at models described in
the industry standard AML [53]. However, we are aware that
this is also limiting the applicability of our approach and
plan to propose extensions. Implementing TMvMT pipelines
using the proposed system architecture requires additional
setup time and effort a priori. However, the integration
effort is then managed through the pipeline and will, once
set up, save time and complexity. Furthermore, validation
of the effectiveness and usability of the TMvMT approach
will require empirical studies with domain experts and their
typical PSE artefacts.
Conclusion andFuture Work
Lost changes, diverging local views, and repetitive manual
integration tasks can lead to late design changes and, thus,
costly errors and mitigation efforts. These issues potentially
influence the process quality in PSE negatively. Identifying
and resolving change conflicts in parallel engineering are
essential to the success of agile PSE. To reduce the risk
of late design changes, this paper aimed at improving
capabilities for traceable multi-view model transformation
for the configuration management of multi-disciplinary
assets and dependencies according to VDI3695-2 [4]. The
synchronization of changes in distributed engineering on
multi-view models depends on capabilities to trace changes
to attribute values in PSE assets back to their sources.
This paper investigated the Position-and-Screw Robot
Cell use case from automotive manufacturing [16] to
identify traceability issues and requirements for multi-
view modelling. To support multi-view changes in PSE,
the TMvMT process and architecture provides required
semantic model analysis capabilities. To this end, the
architecture extends the architecture of the Multi-view
Model Transformation (MvMT) [7]. The goal is to define
traceable and flexible multi-view model transformation
pipelines for building intermediate models and an integrated
PSE model. A main advantage of the approach is its potential
to define discipline-specific model transformations and
integrate multiple view models to an SUM while updating
corresponding views. Another advantage is the modular,
configurable TMvMT approach, building on the EMF
concept with different technologies that are suitable for the
PSE context.
In a feasibility study, we evaluated the TMvMT
approach in the scope of a robot work cell from automotive
manufacturing. We implemented the TMvMT approach
building on the AML standard [53] and automated it with
a Continuous Integration (CI) system. Furthermore, we
compared the traceability capabilities of the approach
to three alternative model transformation approaches in
PSE. The study results indicate that the TMvMT approach
provides a sound foundation for PSE domain experts to
define multi-view models in a traceable way.
Future Work. We plan to investigate different methods
for supporting the construction and validation of the
multi-view engineering graph. Semantic web approaches
and method will be a starting point for this direction.
Furthermore, we will experiment with different graphical
representation forms and approaches such as low code.
Additionally, we will explore possibilities to integrate
Table 2 Traceability
requirements fulfilment with
TMvMT and alternative
approaches, using a 5-point
Likert scale (++, +, o, -, ),
where ++ indicates very high
capability, and very low
Req. Rx/Model Transform. Approach MMT TS-MT MvMT TMvMT
R1. Multi-view modelling capabilities + ++ ++
R2. Distributed process synchronization o + ++
R3. Traceable model change representation - o +
R4. Version representation/management o o +
Effort for model transformation + o + +
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the design model and data with the operational phase to
cover the whole PSE lifecycle. Further, we plan to enable
auditability of data traces for error detection. We will
also investigate how the TMvMT approach will scale up
to larger model sizes, more engineering disciplines, and
larger sets of changes to model elements.
Author contributions Following the contributor roles taxonomy,
we disclose the following contributions by the authors. FR—
conceptualization, data curation, investigation, methodology,
resources, software, validation, visualization, writing—original draft,
and writing—review & editing; LW & KM—conceptualization,
data curation, investigation, methodology, validation, visualization,
writing—original draft, and writing—review & editing; DW—
writing—review & editing, and funding acquisition; AL—investigation,
validation, and funding acquisition; SB—conceptualization, data
curation, investigation, methodology, supervision, validation, writing—
original draft, writing—review & editing, and funding acquisition.
Funding Open access funding provided by TU Wien (TUW). This
study was funded by the Christian Doppler Research Association, the
Austrian Federal Ministry for Digital and Economic Affairs, and the
National Foundation for Research, Technology and Development. This
study was funded by the Austrian Research Promotion Agency (FFG)
via “Austrian Competence Center for Digital Production” (CDP) under
Contract No. 881843.
Availability of data and materials https:// github. com/ tuw- qse/ tmvmt-
resou rces
Code availability https:// github. com/ tuw- qse/ tmvmt- resou rces
Conflict of interest Felix Rinker declares that he has no conflict of in-
terest. Laura Waltersdorfer declares that she has no conflict of interest.
Kristof Meixner declares that he has no conflict of interest. Dietmar
Winkler declares that he has no conflict of interest. Arndt Lüder de-
clares that he has no conflict of interest. Stefan Biffl declares that he
has no conflict of interest.
Ethics approval This article does not contain any studies with human
participants or animals performed by any of the authors.
Consent to participate This article does not contain any studies with
human participants performed by any of the authors.
Consent for publication This article does not contain any third-party
material that requires consent for publication.
Open Access This article is licensed under a Creative Commons Attri-
bution 4.0 International License, which permits use, sharing, adapta-
tion, distribution and reproduction in any medium or format, as long
as you give appropriate credit to the original author(s) and the source,
provide a link to the Creative Commons licence, and indicate if changes
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the article's Creative Commons licence and your intended use is not
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Conference Paper
Full-text available
Cyber-Physical Production Systems (CPPSs) are envisioned as next-generation adaptive production systems combining modern production techniques with the latest information technology. A CPPS creates a complex environment between different domains (mechanical, electrical, software engineering), requiring multidisciplinary solutions to tackle growing complexity issues and reduce (maintenance) effort. Software plays an increasingly important role in assuring an effective and efficient operation of CPPSs. However, software engineering methods applied for CPPSs seem to lag behind modern software engineering methods, where tremendous progress has been made in the last years. We initiated the Software Engineering in Cyber-Physical Production Systems Workshop (SECPPS-WS) to analyze and overcome this gap. After two instances with mostly academic participants, we conducted a full-day workshop with nine industry representatives from eight companies that develop and maintain CPPSs. Each industry representative presented their current work and challenges. We collected these challenges and condensed a categorized list of challenges backed by industry statements and literature. This paper presents the resulting list and pointers to (partial) solutions to offer guidance for academia and identify promising research opportunities in this area.
Conference Paper
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Agile Production Systems Engineering (PSE) is a complex, collaborative, and knowledge-intensive process. PSE requires expert knowledge from various disciplines and the integration of discipline-specific perspectives and workflows. This integration is a major challenge due to fragmented views on the production system with a difficult a priori coordination of changes. Hence, proper tracking and management of changes to heterogeneous engineering artifacts across disciplines is key for successful collaboration in such environments. This paper explores effective and efficient multi-view change management for PSE. Therefore, we elicit requirements for multi-view change management. We design the agile Multi-view Change Management (MvCM) workflow by adapting the well-established Git Workflow with pull requests with a multi-view coordination artifact to improve over traditional document-based change management in PSE. We design an information system architecture to automate MvCM workflow steps. We evaluate the MvCM workflow in the context of a welding robot work cell for car parts, using a typical set of changes. The findings indicate that the MvCM workflow is feasible, effective, and efficient for changes of production asset properties in agile PSE.
Conference Paper
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Background. Consistent cross-disciplinary engineering data models have become increasingly important for engineers and project managers to validate system designs or implement new features in existing systems. However, discipline-specific designs (mechanical, electrical, automation engineering etc.) in isolated data models and proprietary software tools often create information silos. Similar to information systems, the challenges in Cyber-Physical Production System (CPPS) are a high amount of heterogeneous data that needs to be analysed and accessible for stakeholders and systems. Aim. The goal of the Flexible Multi-aspect Model Integration project is to support the integration of local engineering views and artefacts using the definition of common concepts across different disciplines. Therefore, the thesis project will provide capabilities to integrate and validate multi-aspect models more efficiently to increase the data quality. Method. The project will follow Design Science methodology to design and evaluate i) a method for collecting and defining common concepts across engineering disciplines, ii) a modularised software system design that enables flexible model integration processes in a CPPS context, and iii) an exemplary model integration process that supports data integration needs in the planning, operation, and analytics phase. The model integration processes are evaluated with real-world uses cases from industry. Conclusion. The information systems community will gain insight into the requirements in engineering and a method for agreeing on an inter-disciplinary common understanding from this research.
In Production Systems Engineering (PSE), domain experts aim at reusing partial system designs implemented as Industry 4.0 assets and software. However, the knowledge on assets is often scattered across engineering artifacts from multiple disciplines and domain experts, making it difficult to find reusable assets and map them to requirements. In this paper, we (i) identify challenges and requirements for the representation of reuse knowledge in PSE, based on the results of a domain analysis in automotive manufacturing; (ii) refine the Industry 4.0 Asset Network (I4AN) meta-model that integrates multi-disciplinary dependencies between the assets; (iii) introduce the I4AN reference model that exposes recurring patterns; and (iv) present basic and applied patterns for reuse in PSE that aim at improving reuse efficiency and lowering risks. We evaluate the I4AN reference model and patterns with reuse scenarios in a feasibility study in automotive manufacturing. The study results indicate that the I4AN reference model and patterns satisfy the elicited requirements and enable PSE domain experts to identify patterns for reuse and sufficiently complete sets of reusable assets in their contexts.
Conference Paper
Cyber-Physical Production Systems (CPPSs) are envisioned as next-generation adaptive production systems combining modern production techniques with the latest information technology. In CPPS engineering, basic planners define the functional relations between Product-Process-Resource (PPR) views to specify valid production process and resource designs that fulfill the customer requirements. Using the Formalised Process Description standard (VDI 3682) allows to visually model thesePPR views but is hard to process by machines and insufficiently defined formally. In this paper, we present the design of a Domain Specific Language (DSL), the PPR DSL, to effectively and efficiently represent PPR aspects and evaluate constraints defined for these aspects. We illustrate the PPR DSL with the use case rocker switch, abstracted from an industrial use case. We identify requirements and iteratively design and evaluate the PPR DSL. We show that the PPR DSL can model (a) the functional view of CPPSs and (b) define and efficiently evaluate constraints of a CPPS using technologies well-established in industry. We argue that the PPR DSL provides a valuable contribution for the community and industry to describe PPR aspects and evaluate constraints on these aspects. This way, PPR model can be defined and evaluated more easily for researchers and/or practitioners.
Conference Paper
Background. Systems modeling in Production Systems Engineering (PSE) is complex: Multiple views from different disciplines have to be integrated, while semantic differences stemming from various descriptions must be bridged. Aim. This paper proposes the Multi-view Modeling Framework (MvMF) approach and architecture of a model transformation pipeline. The approach aims to ease setup and shorten configuration effort of multi-view modeling operations and support the reusability of modeling environments, like additional view integration. Method. We combine multi-view modeling with principles from distributed, agile workflows, i.e., Git and Continuous Integration. Results. The MvMF provides a lightweight modeling operation environment for AutomationML (AML) models. We show MvMF capabilities and demonstrate the feasibility of MvMF with a demonstrating use case including fundamental model operation features, such as compare and merge. Conclusion. Increasing requirements on the traceability of changes and validation of system designs require improved and extended model transformations and integration mechanisms. The proposed architecture and prototype design represents a first step towards an agile PSE modeling workflow.
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Technical Debt (TD) has proven to be a suitable communication concept for software-intensive contexts to raise awareness regarding longterm negative effects of deviations from standards and guidelines. TD has also been introduced to systems engineering domain, to communicate design shortcomings in long-running, software-assisted systems. We analysed potential TD in the engineering data exchange for production system engineering. Similar to requirements engineering in software-intensive systems, data exchange in the design phase plays an integral part in Software Engineering (SE) for Production Systems Engineering: Specifications, and physical logic have to be derived from heterogeneous plant models or parameter tables designed by different stakeholders. However, traditional procedures and inadequate tool support lead to inefficient data extraction and integration. We identified debt arising from knowledge representation, data model and the exchange process. The refinement validation of identified TD was achieved through semi-structured interviews with representatives in two analysed companies. In an online survey with ten participants from an industrial consortium we evaluated whether the identified TD concepts also applied to other companies, which is true for the majority of TD. Furthermore, we discuss promising TD management strategies to repay and manage negative effects and the accumulation of additional debt, such as improved communication, test-driven model engineering and visualisation of engineering models.