=Paper=
{{Paper
|id=Vol-1511/paper-MPM02
|storemode=property
|title=Towards Inconsistency Management by Process-Oriented Dependency Modeling
|pdfUrl=https://ceur-ws.org/Vol-1511/paper-MPM02.pdf
|volume=Vol-1511
|dblpUrl=https://dblp.org/rec/conf/models/DavidDV15
}}
==Towards Inconsistency Management by Process-Oriented Dependency Modeling==
https://ceur-ws.org/Vol-1511/paper-MPM02.pdf
Towards Inconsistency Management by
Process-Oriented Dependency Modeling
1 1 1,2
István Dávid , Joachim Denil , Hans Vangheluwe
Modeling, Simulation and Design Lab
1
University of Antwerp
Middelheimlaan 1, 2020 Antwerp, Belgium
{istvan.david, joachim.denil, hans.vangheluwe}@uantwerpen.be
2
McGill University, Montreál, Canada
Abstract. Multi-paradigm modeling settings inherently and frequently
cause inconsistencies between models in dierent languages involved in
the design. The proper management of inconsistencies starts with choos-
ing the appropriate formalisms to characterize inconsistent situations. In
this paper, we introduce a formalism, which allows reasoning about in-
consistencies in the context of the design process. We support our ideas
by examples from a case study on the design of an unmanned aerial
vehicle.
Keywords: inconsistency management, process modeling, mechatronic
design
1 Introduction
The design and evolution of mechatronic and cyber-physical systems involves
multiple stakeholders with dierent views on the system as a whole. Because
of overlapping elements and properties between the various views and models,
there is a need to manage the dependencies and possible inconsistencies between
these models [1].
The process of inconsistency management (ICM) breaks down into three main
activities. First, overlap between dierent models is characterized ; based on this
information, inconsistencies can be detected and consequently resolved. Dierent
authors approach this process with various granularity and see the role of char-
acterization (often referred as inconsistency de nition ) dierent. Spanoudakis et
al. do not emphasize the role of proper inconsistency characterization [2], while
Van Der Straeten acknowledges characterization as the foundational rst step
towards a managed approach towards handling inconsistencies [3]. Indeed, the
role of characterization is twofold. First, the inconsistency rules de ned in this
step serve as inputs for the actual detection activity. Second, the formalisms
used to characterize inconsistencies, heavily in uence the choice on techniques
for detection and resolution.
In this paper, we present a novel formalism to characterize dependencies
among dierent models. We argue, that because inconsistencies arise from mod-
2 István Dávid, Joachim Denil, Hans Vangheluwe
els being used in complex design processes, their characterization is to be ap-
proached from a process oriented point of view. This approach considers not just
static relations among models, but also the dynamics of the process. Although
we do not explicitly investigate the subsequent activities of detection and reso-
lution, we give speci c directions on how these should be addressed to exploit
the full potential of our formalism.
Our work is motivated by the speci c nature of a multi-paradigm settings in
the mechatronic domain. Although ICM is an adequately studied eld in soft-
ware engineering, mechatronic design introduces challenges that make software
focused ICM techniques ine cient in mechatronics. This is due to the involve-
ment of models of physics, such as models of nite element methods (FEM) or
computational uid dynamics (CFD). Typically, ICM techniques targeting pure
software systems, even if being situated in a multi-model/multi-paradigm set-
ting, focus on syntactic (structural) inconsistencies arising from the involvement
of detached linguistic meta-models [4].
In a design process with physics involved, however, semantic inconsistencies,
arising from semantic overlaps of various models, have a signi cant impact as
well [5]. While the semantics of a software model are fully to be de ned by the
designer, semantics of models of physics are inherited from a foundational belief
system, such as laws of physics, mechanics, electronics, etc.
The rest of the paper is organized as follows. In Section 2 we motivate our
work using an example scenario from a case study on the design of an unmanned
aerial vehicle (UAV), and present state-of-the-art techniques targeting dierent
aspects of the scenario. In Section 3, we introduce the core ideas of our approach.
Section 4 discusses the related work. Finally, Section 5 brie y discusses the
results and the planned future directions.
2 Process and dependency modeling: the state of the art
In this section, we motivate our work by an example scenario and present the
state-of-the-art solutions tackling well-de ned parts of it.
2.1 Illustrative example
We illustrate our approach using an example from a case study on the design
of an unmanned aerial vehicle (UAV). In the example scenario, the wings of the
UAV are designed using models in various formalisms. The aim is to formalize
the relations among the properties derived from these models either by syntactic
or semantic links (e.g. by simulation).
The design process is sequential and it starts with modeling the overall ge-
ometry using a CAD tool. After validating the model against the requirements,
a nite element method is used to analyse the system. Dierent physical proper-
ties are predicted using a geometric and nite element analysis, for example the
mass of the UAV. The design team than creates a plant model of the UAV using
a lumped parameter model. This plant model is computationally less intensive
Towards ICM by Process-Oriented Dependency Modeling 3
and ideal for creating a control model for the UAV. Finally, a control model is
designed.
By explicit modeling of the design process, valuable information can be
gained on data- and control ows along the elementary design activities. Ad-
ditionally, by explicitly modeling dependencies, inconsistencies among models
in the process can be identi ed. The state-of-the-art addresses these two chal-
lenges separately. We argue, that combining these techniques yield signi cant
advantages in managing model inconsistencies.
2.2 FTG+PM: a formalism for process modeling
The Formalism Transformation Graph and Process Model (FTG+PM) formal-
ism [6] is a framework for modeling processes in the presence of a strong type
system: the FTG, encompassing languages and transformations which processes
are built upon.
ModelGeometry
CAD :ModelGeometry
FEMTrace geometry:CAD geo:Physical
CAD2FEM PropertyLanguage
:FEMAnalysis_
Combined
FEMAnalysis
GetPhysicalProperties FEM
:DesignPlant
PhysicalProperty DesignControl plant:Simulink
Language
:GetPhysicalPro
DesignPlant perties
GetPhysicalProperties plant:Physical
PropertyLanguage
:DesignControl
Simulink control:Simulink
Fig. 1: FTG+PM of the example.
Figure 1 shows the FTG+PM model of the example. The FTG on the left side
of the gure presents the formalisms involved in the scenario and the transfor-
mations between those. Formalisms type artefacts in the PM (on the right side
of the gure). For example, the CAD formalism of the FTG types the geometry
artefact in the PM.
An advantage of the formalism is the explicit type system attached to the
process model, which enables reasoning about model properties and qualities
4 István Dávid, Joachim Denil, Hans Vangheluwe
on an additional meta-level as compared to process modeling frameworks like
BPMN [7].
2.3 Modeling dependencies among models
Qamar et al. investigate inconsistency management in the mechatronic domain
and argue that model-based techniques are an appropriate means for inconsis-
tency characterization [1].
The work interprets se-
mantic overlaps as dependen-
cies among elements of dier-
ent models and captures these
in a correspondence model,
called the dependency model
(DM). Figure 2 shows such
a DM. Synthesis properties
(SP) and analysis proper-
ties (AP) are distinguished
to capture wether properties
represent a system alternative
Fig. 2: A dependency model as presented in [1].
at design time, or a result of
an analysis, respectively. Sim-
ilarly, synthesis dependencies (SD) represent a choice made by the designer, re-
sulting in an SP; while analysis dependencies (AD) represent predictive models
to calculate an AP.
In this paper, we do not distinguish between synthesis and analysis properties
and dependencies, as the formalism presented later is applicable for each of these.
3 Process-oriented modeling of dependencies
Our approach combines the ideas presented in Section 2.2 and Section 2.3. First,
we generalize and extend the dependency modeling technique and rede ne it in
the context of processes modelled using the FTG+PM formalism.
3.1 Levels of precision
The nature of model dependencies in design processes is often well-known for
participating stakeholders. In fact, this knowledge often goes beyond the level
of simple in uence-like relationships: dependencies encompassing sensitivity and
exact mathematical relationships are also common in practice because of domain
knowledge and experience.
Explicit modeling of this information enables more complex formal reason-
ing about model dependencies as compared to [1]. Therefore, we extend the
dependency modeling formalism by introducing explicit levels of precision to
dependency properties. We distinguish between three potential levels a set of
model elements can in uence another set of model elements.
Towards ICM by Process-Oriented Dependency Modeling 5
L1: in uence graphs. The rst level is equivalent to the information de-
picted in [1]. The only information available is the structure of the depen-
dency model, i.e. the vertices and edges. It enables reasoning over model
elements being connected by semantic relationships.
L2: sensitivity. In engineering practice, it is a common scenario that not
only the semantic relationship is known among various model elements, but
also the sensitivity. By simulation and analysis, further knowledge can be
gained on sensitivity information [8]. From the example in Section 2.1, such
an information can be the in uence of the dimensions of the controllable
surface of the wings on the parameters of the control model.
L3: exact mathematical relationship. The highest level of precision is
a mathematical relationship of the depending model elements. We foresee
algebraic relationships being the typical examples here. For example, in Sec-
tion 2.1, the mass of the UAV can be derived as the mass of all the model
elements featuring a mass property, i.e. the airframe, the wings and other
hardware. Other types of mathematical relationships, such as dierential
equations can also be used as an L3 relationship.
3.2 A metamodel for modeling dependencies with levels of precision
To enable modeling with levels of precision, we propose a metamodel shown in
Figure 3. The metamodel builds on [1], but (i) places it into the context of a
design Process, and (ii) introduces the notion of levels.
Fig. 3: Metamodel of dependency models with explicit levels of precision (L1L3)
Process es comprise Activities, which take Models as input and output artefacts.
In engineering design processes these models are the ones describing various parts
of the virtual product. Models feature syntactic and semantic Properties. The
former one refers to model elements derived from a linguistic metamodel, while
the latter ones are typically obtained by simulation or analysis. The Dependency
Model is a special kind of model, which features Dependencies, which, in line
with the de nition in Section 2.3, connect a set of input with a set of output
Properties. Additionally, Dependencies are re ned into L1, L2, L3 dependencies,
based on the level of precision.
6 István Dávid, Joachim Denil, Hans Vangheluwe
3.3 Combining FTG+PMs with DMs
:ModelGeometry
geometry:CAD
geo:Physical
:FEMAnalysis_ PropertyLanguage
Combined
controlSurfaceDep:L1
:DesignPlant
controlSurfaceDep:L2
plant:Simulink massDep:L1 stiffnesDep:L2
:GetPhysicalPro massDep:L3 stiffnesDep:L3
perties
plant:Physical
PropertyLanguage
:DesignControl
control:Simulink
Fig. 4: PM of the example extended with DM information.
Figure 4 shows the process model of the example scenario, extended by de-
pendency information. Three dependency properties between the language of
geometrical properties and the language of physical properties are depicted us-
ing directed edges of L1L3 levels of precision. (Every directed dependency edge
is typed in the FTG, which is not presented hereby.) For example, there is an L3
relationship between the mass of the wing and the mass considered in the con-
trol model, in this direction. This dependency link allows propagating changes
from the geometrical model through an exact mathematical relationship to the
control model. In the other direction, however there is only an L1 relationship,
meaning if the mass in the control model is changed, the changes cannot be
propagated directly to the geometry model, but there is an indication that they
should be.
The generality of this approach
is shown using Figure 5. The gure property c property d
dependency
shows the relation between properties
of submodels. By using slicing tech- property a property b
niques, a relation is established be- has
tween a part of the model and a cer-
submodel B’
tain property. For example, in our ex-
ample we could be interested in only submodel A’ slice
the mass of the wing of the UAV. By model A model B
slicing the model to only contain the
wing, we can use the geometrical and Fig. 5: Properties related to submodels
material information to establish a re- by using slicing.
Towards ICM by Process-Oriented Dependency Modeling 7
lation with only the wing segment of the CAD model. The slicing can also occur
during the analysis step. This leaves the relation between a property and the
submodel implicit. For example, because the analysis is black-box. Leaving the
relation between the submodel and the property implicit could have an in uence
on how resolution strategies can cope with an inconsistency.
3.4 Typical uses of the technique
We show two typical uses of our technique to motivate its usefulness in complex
design processes.
Inconsistency characterization, detection and resolution Our approach
is situated in a conceptual inconsistency management framework as an impor-
tant formalism to build inconsistency management processes upon. Since the
dependency model of our approach constitutes a graph (more speci cally: a hy-
pergraph), characterizing inconsistencies as graph patterns over the graph of
dependencies, seems to be a natural t. Additionally, graph patterns can be
evaluated at run-time very e ciently and therefore, it gives a well-performing
technique to detect inconsistencies [9]. The way these graph patterns can be
employed in a speci c ICM technique, depends on the level of precision depen-
dencies are captured on.
Qamar et al. use L1 dependencies to identify potentially inconsistent states [1].
If an input element to a dependency is changed, the output elements become
potentially inconsistent and manual inspection of these is required to maintain
a consistent state. In the background, the in uence graph is captured by graph
patterns; a change in the match set of a graph pattern is interpreted as a poten-
tial inconsistency.
L2 dependencies extend this approach by giving additional hints on the mag-
nitude of inconsistency in the output property. Therefore, L2 dependencies en-
able reasoning about the quality of the process topology and optimize it for
consistency. (Section 3.4)
Since L3 dependencies extend the former two by de ning an exact mathemat-
ical relationship among model elements, they enable a semi-automated detection
of inconsistencies by continuously evaluating these relationships.
Process optimization Dependency information can help restructure processes
into more e cient alternative topologies.
A typical example of such an optimization can be making sequential design
processes parallel in order to speed up processes, by identifying activities hav-
ing no data- or control dependency, but featuring explicitly modelled semantic
dependencies. Apart from the former two constraints, the semantic dependency
model ensures inconsistencies will be managed over parallel branches as well.
Techniques based on change propagation, incremental synchronization typically
focus on sequential sub-process topologies and therefore, they fall short to tackle
inconsistencies in parallel settings. L1L3 dependencies all support ICM over
8 István Dávid, Joachim Denil, Hans Vangheluwe
parallel branches, although the e ciency of ICM is determined by the level.
Figure 6 shows the parallel equivalent of the PM in Figure 4.
concept:DesignCon
cept
:DesignConcept
concept:Physical
PropertyLanguage
:GetPhysicalPro
controlSurfaceDep:L3
perties stiffnesDep:L3
controlSurfaceDep:L1
stiffnesDep:L1
massDep:L3
massDep:L1
:ModelGeometry :DesignPlant
geo:Physical plant:Physical
geometry:CAD plant:Simulink
PropertyLanguage PropertyLanguage
:FEMAnalysis_ :GetPhysicalPro
Combined perties
stiffnesDep:L2 controlSurfaceDep:L1
stiffnesDep:L3 controlSurfaceDep:L2 :DesignControl
massDep:L1
… massDep:L3 control:Simulink
…
…
…
Fig. 6: Parallel equivalent of the sequential PM of the example.
L2 relationships can help further optimizing processes for decreasing the amount
of inconsistencies encountered, e.g. by moving high-impact activities in the earlier
phases of the process and therefore, potentially reducing the amount of required
re-iterations. The lifting of design choices to a higher level of abstraction to allow
for parallel design also hints towards the use of viewpoint contracts in the design
process, similar to Toerngren et al [10].
Finally, organizing processes in a topology where higher level dependencies
point forward in the process increases the e ciency of ICM techniques based on
change propagation.
4 Related work
Characterization of model inconsistencies has been approached using various
techniques, such as description logic based [3], rule-based [11] and model-based.
Triple graph grammars (TGG) have been widely used as a formal underpin-
ning to model-based ICM techniques [12]. Adourian and Vangheluwe present a
technique for characterizing and detecting semantic consistencies between geo-
metric and dynamic views of mechanical systems based on TGGs [5]. Further-
more bidirectional synchronization techniques are often used to resolve incon-
sistencies [13]. The structure of TGG formalism, however, inherently prevents
expressing multi-level connections among model elements, as opposed to the
FTG+PM formalism used in our work. As an implementation of the principles
Towards ICM by Process-Oriented Dependency Modeling 9
of megamodeling [14], the FTG+PM formalism enables lling semantic gaps, as
highlighted in Section 3.3.
Design structure matrices (DSM) [15] are used to model structures of complex
processes or systems. Clustering techniques for DSMs improve the design and
manufacturing processes [16]. By introducing levels of precision, our technique
enables reasoning about quality of process topologies in a more detailed way.
Herzig et al. provides support for automating the task of inconsistency char-
acterization by exploring semantic relationships using similarity metrics [17].
Qamar et al de ne ve levels of detail of modeling dependencies [18]. As
opposed to levels of precision, which capture how precise the applied dependency
modeling formalisms are, levels of detail capture the extent of the dependency
modeling being applied in a design process. More speci cally, our work is situated
on Level 2 of detail, where dependencies are formally captured through a model.
5 Conclusions
In this paper, we introduced a novel formalism for characterizing and detecting
inconsistencies among design models. Our work was motivated by the speci c
nature of multi-model settings in the mechatronic domain. The approach extends
the dependency modeling technique of [1] and places it into the context of design
processes. As a process modeling formalism, we employed FTG+PM [6]. As
shown in Section 3.3, an important bene t of this formalism is that its FTG
part lls a logical gap between the metalevels of processes and our dependency
modeling language.
The main advantage of our work is that inconsistencies can be characterized
using a graph-like structure, which enables using state-of-the-art graph querying
tools to evaluate (or enforce) model consistency.
As a future work, we plan to further evolve the formalism by including au-
thorization and ownership models of design processes and therefore, enable rea-
soning about the limits of automation of inconsistency resolution techniques and
including explicitly modelled manual steps in the resolution process.
Acknowledgement This work has been carried out within the framework of the
MBSE4Mechatronics project (grant nr. 130013) of the agency for Innovation by
Science and Technology in Flanders (IWT-Vlaanderen).
The authors wish to thank the insightful comments of Johan Vanhuyse, Tuur
Benoit, Klaas Gadeyne and Maarten Witters.
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