=Paper=
{{Paper
|id=Vol-1722/p2
|storemode=property
|title=A Feature-based Comparison of Melanee and Metadepth
|pdfUrl=https://ceur-ws.org/Vol-1722/p2.pdf
|volume=Vol-1722
|authors=Ralph Gerbig,Colin Atkinson,Juan de Lara,Esther Guerra
|dblpUrl=https://dblp.org/rec/conf/models/GerbigALG16
}}
==A Feature-based Comparison of Melanee and Metadepth==
https://ceur-ws.org/Vol-1722/p2.pdf
A Feature-based Comparison of Melanee and
MetaDepth
Ralph Gerbig1 , Colin Atkinson1 , Juan de Lara2 , and Esther Guerra2
1
University of Mannheim
{atkinson, gerbig}@informatik.uni-mannheim.de
2
Universidad Autonoma de Madrid
{Juan.deLara, Esther.Guerra}@uam.es
Abstract. Melanee and MetaDepth are two deep modeling tools based
on a common set of core concepts such as the orthogonal classification
architecture and deep instantiation. However, at the present time, they
have different foci. MetaDepth is a textual modeling tool focusing on
the needs of software developers while Melanee is a graphical modeling
tool focusing on modeling language creation. The question arises, there-
fore, as to how different/similar the tools actually are and how their
features relate to each other. This paper addresses this question by com-
paring the two tools and investigating the extent to which the modeling
features they offer to users overlap.
Keywords: Multi Level Modeling; Deep Modeling; Melanee; MetaDepth
1 Introduction
Melanee [5] and MetaDepth [12] are two of the maturest, publicly available
deep modeling tools explicitly built on the notions of orthogonal classification
and deep instantiation. Over time they have evolved to specialize on two dif-
ferent aspects of model language engineering and use. MetaDepth is primar-
ily aimed at supporting programmers who want to create deep models using a
textual syntax and write executable code against them. Melanee, on the other
hand, is primarily aimed at modelers who want to create and use domain spe-
cific languages in multiple formats such as table, text and graph-based diagram.
Execution of these models is not the main focus of Melanee at the moment, but
constraint and action languages are under development at the time of writing.
As far as the data modeling features of the two tools are concerned, they seem
to be very similar despite their focus on different target audiences. They are both
based on the orthogonal classification architecture [3], and both support the core
notions of clabjects [1] and potency [7]. However, they do so in different ways,
with some distinct concepts and semantics. Modelers who wish to develop deep
models using these concepts are therefore faced with the problem of working
out precisely how the tools differ and which tool best suits their needs. The
goal of this paper is therefore to clarify the similarities and differences between
Melanee’s and MetaDepth’s underlying modeling approaches as well as the set
of features they offer for users. The result of the paper is a list of deep modeling
features, presented using the tools in different modeling scenarios found in the
literature, and a discussion of the relevance of these features for specific modeling
goals.
The paper is structured as follows. The next section clarifies the terminology
used in this paper since the two tools sometimes use different terms for the same
concept. This is followed by a comparison of the tools’ modeling architectures
(Section 3), support for deep instantiation (Section 4), handling of attributes
and edges (Section 5) and inheritance rules (Section 6). The paper then presents
a small summary of the results of the comparison in a table (Section 7) and a
discussion of their significance. Finally, Section 8 concludes with some closing
remarks.
2 Terminology
When comparing two modeling tools that sometimes use a different vocabulary
for similar concepts, it is important to define the terminology used. Hence, this
chapter highlights the terminological differences in the two tools’ flavors of deep
modeling and explains which terminology is adopted for the remainder of the
paper.
The three core terms used to refer to modeling content in MetaDepth are
Model, Node and Edge, while Melanee uses the terms Level, Entity and Connec-
tion to refer to the same basic concepts. Of these, the term Model probably has
the potential to cause the most confusion. In MetaDepth a Model is the con-
tainer for all model elements in a single classification level, whereas in Melanee
the term Model, or more specifically Deep Model, encompasses all classification
levels (i.e. models). In Melanee, the term used to refer to a container for content
at a given ontological Level is Level. To avoid confusion, in the remainder of the
paper we use the term Level to refer to container for model content at a given
ontological level, and Model for the union of all ontological levels.
There are also differences between Melanee and MetaDepth in the terms
used to refer to level content. In particular, model elements representing class-
es/objects in the problem domain are named Nodes in MetaDepth and Enti-
ties in Melanee, while model elements representing links/associations are called
Edges in MetaDepth and Connections in Melanee. To avoid confusion, in the
rest of the paper we use the MetaDepth terms – namely, Node and Edge.
Both tools regard Nodes and Edges as Clabject, and employ the term potency to
describe over how many subsequent classification levels a clabject can be instan-
tiated. Hence, these two terms (Clabject and potency) are used in the following
without ambiguity.
Another difference of terminology between MetaDepth and Melanee is the
use of attribute and field to refer to the same thing. Melanee uses the term
Attribute, while MetaDepth uses Field. In the following, the term Attribute is
used as it is widely used in the UML technology space. Finally, the MetaDepth
literature uses a technical term, linguistic extension, for introducing new types
into a model at levels which are not the most abstract. We also use this term
with the same meaning in the rest of this paper.
3 Modeling Architecture
Figure 1 shows canonical representations from the literature of the MetaDepth
architecture (Figure 1(a)) and Melanee architecture (Figure 1(b)) side-by-side.
The two figures show that both approaches are based on the orthogonal clas-
sification architecture (OCA) in which one level-spanning meta-model classifies
the content of the ontological levels from a linguistic (i.e. tool) point of view. In
the MetaDepth version of the OCA (Figure 1(a)), the linguistic meta-model
is displayed on the left of the figure labeled Linguistic meta-model , and the on-
tological levels are arranged vertically to the right and use the @ -notation to
indicate their potency. The Melanee version of the OCA (Figure 1(b)) displays
the linguistic meta-model at the top of the figure labeled L2 , while the ontological
levels are arranged horizontally and are labeled using the O prefix, e.g. O0 . The
Melanee example additionally shows the real world which is modeled at level L0 .
This real world exists in the MetaDepth architecture too but is not displayed
in Figure 1(a). In both architectures, model elements are not only classified by
their linguistic types but can also be classified by an ontological type residing
at a higher ontological level. Multiple ontological classification is not allowed in
MetaDepth but is allowed Melanee. In [11], however, a mechanism is presented
for retyping a clabject with respect to several other clabjects after creation from
one ontological type.
Linguistic meta-model @2
Element
Task
name
name@ 1: S tring
instanceOf
ontological
start: Date Level Method
* Clabject Feature Attribute
* Instance ontological @1 content
Type instanceOf potency durability mutability
L2 * feature
«linguistic instanceOf»
potency: int
E valuation: Task O0 O1 O2
1
name=“evaluation” Task2 Evaluation Exam0
supers * subject: S tring name1:String name0:String=evaluation subject0:String=Maths
C labject L1 start2:Date start1:Date start0:Date=22/10/13
* ontological @0 subject1:String
* instanceOf
Field exam: E valuation
subject=“Maths” ??? Evaluation
start=“22/10/12” L0
(a) (b)
Fig. 1. MetaDepth architecture (a) from [14] and Melanee architecture (b) adapted
from [6].
It can be observed that both linguistic meta-models share the concept of
Clabject which is a concatenation of the words Class and Object. This term reflects
the type instance duality of model elements residing in the middle classification
levels. They are at the same time instances of their types at the ontological
level above and types for their instances one ontological level below. In the
MetaDepth version of the OCA, this dual nature of clabjects is explicitly
modeled by defining Clabject as a subclass of both Type and Instance .
For one ontological type to conform to another in these architectures, certain
conditions have to be fulfilled by the instances. The set of attributes of the
instance has to conform to the set of attributes defined by the ontological type,
the connection cardinalities defined by the ontological type have to be satisfied
by ontological instances, and the rules for potency have to be satisfied. These
three criteria are the same in MetaDepth and Melanee.
4 Deep instantiation
Melanee and MetaDepth are both based on deep instantiation. MetaDepth
allows a potency for levels, clabjects, and fields to be configured while Melanee
allows potencies to be defined for clabjects, operations, attributes and their val-
ues. The fact that levels do not have potencies in Melanee has the advantage
that an unlimited number of classification levels can be defined without thinking
of the deepness of a deep model when assigning a potency to the initial classifica-
tion level. However, it has the disadvantage that potencies have to be defined for
each clabject in the level. Futhermore, MetaDepth allows models (i.e. levels)
to be imported into other models for reuse. In Melanee only level content such
as clabjects and packages can be linked.
The linguistic meta-model element, Operation, shown in the linguistic meta-
model of Melanee in Figure 1(b) is not available in MetaDepth. Hence, in
MetaDepth only attributes can be added to clabjects.
Potencies facilitate deep instantiation [8, 10] by expressing the deepness of a
model element’s instantiation tree, i.e. how many subsequent ontological levels
it can influence through its classification tree depth and through the existence
and values of attributes over that tree. However, there are differences in the way
Melanee and MetaDepth define potency for model elements. In MetaDepth
the potency is initially associated with levels as indicated in Figure 1(a) and
then applied to the level content, i.e. clabjects and attributes . In other words,
the default value for clabject potencies in MetaDepth is the potency of the
level. Attributes inherit their default potency from their containing clabjects .
Clabjects can have a potency differing from their level’s potency, but this has
to be explicitly stated. On the contrary, in Melanee, the potency is explicitly
defined for each clabject and attribute as shown in Figure 1(b).
The rules for potency reduction during instantiation are identical for Melanee
and MetaDepth. Instances always have a potency that is one lower than the
potency of their type. Model elements with a negative potency cannot exist,
and so, model elements with a potency value 0 cannot be instantiated. They
therefore correspond to concepts such as objects and slots, when compared to
the UML technical space.
Both tools allow instantiation over an arbitrary number of levels using so-
called star potency. When instantiating a model element with star potency, the
instances can have a star potency themselves or a potency with a non-negative
Integer value. This is an advantage when defining frameworks in the form of a
modeling language for instantiation. In such a scenario, a modeler often does not
know in what environment the framework will be used and thus cannot define
appropriate constraints on the instantiation of the provided types.
Strict meta-modeling [4] requires each instance to have a type exactly one
level above. This may lead to identity instantiations where clabjects are instan-
tiated at each ontological level without adding additional information, just for
the pure reason of supporting the instantiation of further elements at the level
below. To overcome this problem, MetaDepth offers a special feature exclu-
sive to the tool called leap potency [13]. If a type is assigned leap potency n,
then instances of the type can be created n levels below, eliminating the need
to instantiate the type at intermediate levels.
Linguistic extension, the ability to add new types at any intermediate level
without being classified by an ontological type, is supported by both tools.
5 Attributes and Edges
There are some significant differences between MetaDepth and Melanee when
it comes to the definition of attributes and edges. In MetaDepth an attribute
does not have to be present (repeated) at all intermediate levels. The only level
at which an attribute is mandatory is the lowest level, where it plays the role
of a slot. However, attributes can also be added to intermediate levels to hold
default values. In Melanee, an attribute has to be present throughout the whole
instantiation tree of a type. Melanee, in contrast to MetaDepth, allows finer-
grained control of attributes by assigning a value potency (mutability) to them
in addition to the attribute potency (durability). The durability defines over how
many subsequent levels an attribute can exist (i.e. endure) and the mutability
defines over how many levels the value of an attribute can change. Together
they allow modelers to capture precise details about the ownership, visibility
and changeability of attributes across classification levels, akin to features such
as tags, tag values, statics variables and constraints in the UML space. For
example it is possible to define that an attribute belongs only to the type facet
of a clabject and thus is not derived by its instances (by assigned it durability 0),
or that an attribute’s default value defined at the class level cannot be changed
by its instances (by assigning it mutability 0).
MetaDepth offers richer functionality than Melanee when it comes to con-
necting clabjects with each other. Melanee only allows edges to be represented
by connection classes, where each Edge is a fully fledged clabject containing
attributes, operations and connection ends identifying the connected clabjects.
MetaDepth goes beyond this by also allowing edges to be represented by simple
references which are essentially attributes whose value is a reference to another
clabject.
Both approaches support connection diversification [2] as they allow connec-
tion ends / reference-names to be renamed at the instance level. Melanee allows
edges to represent containment and aggregation as well as basic associations,
whereas MetaDepth is only aware of basic associations. Also, MetaDepth
does not allow clabjects to contain other clabjects like Melanee.
MetaDepth supports deep references (Figure 2) which are not available in
Melanee. Deep references help when nodes need to be connected but the direct
types are not known at the time of modeling. In Figure 2, for example, a Page
Presentation Component
view @1 view @2
DEEP
@0
LANGUAGE P age * C omponent
DEFINITION
@1
DEEP
LANGUAGE TableVisualizer: …
USAGE JavaS cript
@0 @0
DEEP
LANGUAGE mapTable:
p1: P age
USAGE TableVisualizer
Fig. 2. Deep reference example from [13].
instance shall be connected with a Component instance. Component however is
instantiated at one more level in contrast to Page resulting in potency 0 Page
instances being connected to indirect potency 0 instances of Component . In the
example Component is instantiated as TableVisualizer . The Page instances shall be
connected with instances of this direct instance of Component . Deep references
in MetaDepth support the creation of such references whose direct type is not
currently known. By assigning @0 to the reference between Page and Component
it is declared that Page instances shall be connected with indirect Component
instances of potency 0.
The multiplicity (cardinality) semantics of MetaDepth restricts the number
of instances of a connection at the level below. Each instance then defines its own
multiplicity for the following level. In Melanee, the multiplicity constraint of the
type level is satisfied if the sum of the multiplicities of all connection instances
is in the range of the multiplicity defined at the type level. Hence, a multiplic-
ity constraint not only constrains the immediate level below but all following
ontological classification levels. Approaches providing finer grained control of
multiplicity to Melanee are also feasible as described in [2].
Enumerations can be defined in both Melanee and MetaDepth. Melanee
allows an enumeration to be defined in a deep model and then used by clabjects
across all classification levels. MetaDepth defines enumerations within levels
which can then be used at all subsequent levels.
6 Inheritance at intermediate levels
Melanee and MetaDepth follow similar rules concerning inheritance. Subtypes
must have a potency equal to or higher than their supertype. Attributes can be
repeated in subtypes in order to override values defined in supertypes. Inheri-
tance relationships can exist at all levels including intermediate levels and the
most concrete level. Hence, inheritance works for both type and instance facets
of clabjects, enabling a sort of prototype-based modelling. Moreover, multiple
inheritance is supported by both approaches.
When it comes to typing of participants in an inheritance relationship Mela-
nee and MetaDepth differ. Unlike MetaDepth, Melanee allows supertypes
1 Model O0@2{
O0 2 Node EmployeeType {
EmployeeType 2 3 name : S t r i n g ;
name2 4 salary : String ;
salary2 5 }
O1 6 }
CivilServant 0 7
seniority1 8 O0 O1{
9 abstract EmployeeType C i v i l S e r v a n t {
10 seniority : String
11 }
12 EmployeeType O n l i n e M a r k e t i n g : C i v i l S e r v a n t {
OnlineMarketing1:EmployeeType 13 name=”O n l i n e M a r k e t i n g ” ;
14 }
name1=Online Markteing
salary1
15 }
16
O2 17 O1 O2{
18 O n l i n e M a r k e t i n g Jim{
Jim0:OnlineMarketing 19 name=”Jim ” ;
name0:String=Jim 20 s a l a r y =”32k ” ;
salary0:String=32k 21 }
22 }
(a) (b)
Fig. 3. Inheritance example in Melane (a) and MetaDepth (b).
which do not have an ontological type. MetaDepth always forces a supertype
to have an ontological type which is compatible with the ontological type of its
subtypes. Compatible means that the ontological type of the supertype has to be
equal to, or a supertype of, the ontological type of the subtype. Hence, the Mela-
nee inheritance example shown in Figure 3(a) is not allowed in MetaDepth. To
make the example valid in MetaDepth CivilServant would have to be assigned
EmployeeType as its ontological type as shown in the MetaDepth version of the
example in Figure 3(b).
This restriction prevents the definition of abstract supertypes with a potency
of 0 if the subtypes have ontological types in MetaDepth. In Melanee this tech-
nique is commonly used to represent abstract classes, especially in the context
of the power type pattern. In MetaDepth, an abstract class always has to be
ontologically typed and have a potency one less than the potency of the ontolog-
ical type. In most cases this new potency is different to 0 for instances residing
in middle classification levels. To overcome this problem the abstract keyword is
introduced in MetaDepth’s textual syntax as shown in Figure 3(b).
The difference between MetaDepth’s programming language orientation
and Melanee’s structural modeling orientation can be seen in the example in
Figure 4. MetaDepth does not allow inheritance in the middle level of the
example where the supertype (A1 ) is an ontological instance of a different onto-
logical type, A, to the subtype (B1 ), which is of type B . The problem is that A
and B are not related by inheritance at the top ontological level. MetaDepth
prevents this because it is possible that a constraint written on A is applied
to instances of B in such a model as shown in Figure 4. Such behavior is not
easily foreseen by programmers and is thus prohibited in MetaDepth in favor
of simpler and more reliable constraint definition. Melanee, however would al-
@2 @2
A B
A1: A
B1: B
a: A1 b: B1
Fig. 4. Inheritance example from [13].
low such a situation since B could be assumed to be a subtype of A in a case
where the sets of instances are overlapping and hence it is correct to have the
same constraint also applied to instances of B . Melanee’s reasoning services [9]
could even discover an inheritance relationship between A and B based on their
property sets.
In addition to pure generalizations as supported in MetaDepth, Melanee
adds the ability to define generalization sets which can mark the instance set as
complete/incomplete and overlapping/disjoint. Furthermore, names (a.k.a dis-
criminants) can be given to these generalization sets.
7 Comparison Summary
In the previous sections the languages of Melanee and MetaDepth were com-
pared in terms of their implementation of potency, their handling of attributes
and edges, classification semantics, and their semantics for inheritance. The re-
sults are summarized in Table 1 and Table 2. This shows that the number of
common features (highlighted cells) roughly matches the number of differences
in all four of these areas.
MetaDepth Melanee
Potency Reduction – Potency at instance one equal to MetaDepth
lower than at ontological
type level
– potency 0 clabjects can-
not have instance
Leap Potency available not available
Potency
Star Potency available available
Strict Meta Modeling – leap potency enforced
– deep references
Potency Declaration – At level, clabject At each clabject
– clabjects inherited po-
tency from level if not
stated otherwise
Abstract Keyword available no keyword, potency of 0
Table 1. Summary of the comparison part 1
MetaDepth Melanee
Potency subtype potency equal or equal to MetaDepth
higher supertype
Disjoint Types of su- not allowed allowed
Inheritance
per and subtype
Untyped supertype for not allowed allowed
typed subtype
Multiple Inheritance supported supported
Generalization Sets not supported supported
Attribute Overriding by duplication equal to MetaDepth
Durability derived from Clabject, but set independent from Clab-
can be set indivually ject
Mutability not available available
Attributes, Edges & Distributed Modeling
Repeated at instances not mandatory mandatory
Default Values at In- available available
termediate Levels
Association Classes available only available kind of connec-
tions
References (complex available not available
attributes)
Cardinality effects immediate level below effects all levels below
Containment & Own- not available available
ing
Association Types plain associations only containment, aggregation,
plain association
Connection Diversica- supported supported
tion
Enumerations supported supported
Operations not available available
Distributed Modeling importing of Levels (i.e. linking to remote Clabjects
Models in MetaDepth) and Packages
Clabject Conformance – potency equal to MetaDepth
Classification
– attributes
– edges & references
Multiple Ontological dynamic retyping [11] available
Classification
Linguistic Extension allowed allowed
Table 2. Summary of the comparison part 2
8 Conclusion
This paper has presented a comparison between the Melanee and MetaDepth
approaches to deep modeling and revealed that even though the two languages
might look quite similar at first sight and share a lot of common ideas, they have
significant differences in their detailed terminology and semantics. The next step
is to extend the comparison to include a broader range of deep modeling tools
in order to create a concrete set of criteria for comparing them. Once such an
exhaustive set of features has been defined it can be used as the basis for empiri-
cal studies into which language features should be targeted to which user groups
and for defining guidelines for choosing deep modeling tools. We are working on
building transformations between the two tools to bridge their semantic differ-
ences. We hope this paper may serve as a starting point for providing criteria to
compare deep modeling tools, and encourage other researchers to compare their
tools to MetaDepth and Melanee, providing further comparison points.
Acknowledgements. Work partially sponsored by the Spanish MINECO (TIN-
2014-52129-R), and the Madrid Region (S2013/ICE-3006).
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