Model Transformation by Example with Statistical Machine Translation

Karima Berramla

1,3 a

, El Abbassia Deba

1 b

, Jiechen Wu

2 c

, Houari Sahraoui

2 d

and Abou El Hassen Benyamina

1 e

LAPECI Laboratory, Oran 1 University Ahmed Ben Bella, Algeria

DIRO, Universit

e de Montr

eal, Canada

Ain Temouchent University Center, Algeria

Keywords:

Model Transformation, Model Transformation by Example, Learning System, SMT System, IBM1 Model.

Abstract:

In the last decade, Model-Driven Engineering (MDE) has experienced rapid growth in the software devel-

opment community. In this context, model transformation occupies an important place that automates the

transitions between development steps during the application production. To implement this transformation

process, we require mastering languages and tools, but more importantly the semantic equivalence between

the involved input and output metamodels. This knowledge is in general difﬁcult to acquire, which makes

transformation writing complex, time-consuming, and error-prone. In this paper, we propose a new model

transformation by example approach to simplify model transformations, using Statistical Machine Transla-

tion (SMT). Our approach exploits the power of SMT by converting models in natural language texts and by

processing them using models trained with IBM1 model.

1 INTRODUCTION

Model transformation is not a new activity. It has

been recognized as an essential component since the

appearance of application development techniques,

but it has essentially been implemented using gen-

eral purpose programming languages. The emergence

of the Model Driven architecture (MDA) (Kleppe

et al., 2003) paradigm has given another perspective

to model transformation by the introduction of dedi-

cated languages and tools.

Although dedicated languages and tools advanced

considerably the state of the practice, deﬁning model

transformations still requires a lot of effort and time.

The current challenge in MDE is not only (i) how to

choose the best technique through which we deﬁne

and generate models and metamodels in a simple way

but also (ii) how to automate transformation process,

to alleviate the burden on developers who has to deal

with a variety of domain speciﬁc languages in which

https://orcid.org/0000-0002-2847-4895

https://orcid.org/0000-0003-2948-2093

https://orcid.org/0000-0002-4011-0859

https://orcid.org/0000-0001-6304-9926

https://orcid.org/0000-0003-4778-0123

models are expressed.

In this paper, we propose a new approach based

on a statistical machine translation (SMT) system for

model transformation by example. This approach

has several advantages. Firstly, it allows transform-

ing models without using a transformation language

that requires mastering its syntax and the semantic

between source and target metamodels. Secondly, it

reuses SMT, a technique with a proven track record

in natural language translation. Although SMT is

mainly used for natural language translation, this

technique was already used to solve some domain-

speciﬁc transformation problems, but in an ad-hoc

manner. An interesting example of such a usage is

the work by Alghawanmeh et al. (Al-Ghawanmeh

and Sma

ıli, 2017), which translates Arab vocal im-

provisation to instrumental melodic accompaniment.

We evaluated our approach on varous transformation

examples. The results show that SMT-based model

transformation can be used successfully with a rea-

sonable number of examples.

The remainder of this paper is organized as fol-

lows. Section 2 deﬁnes the crucial problem of model

transformations. The proposed approach based on a

statistical machine translation system is presented in

Section 3 and a case study illustrates it in the sec-

Berramla, K., Deba, E., Wu, J., Sahraoui, H. and Benyamina, A.

Model Transformation by Example with Statistical Machine Translation.

DOI: 10.5220/0009168200760083

In Proceedings of the 8th International Conference on Model-Driven Engineering and Software Development (MODELSWARD 2020), pages 76-83

ISBN: 978-989-758-400-8; ISSN: 2184-4348

Figure 1: Model transformation problems.

tion 4. Section 5 describes the evaluation of the ap-

proach. In Section 6 the related work is analyzed and

discussed. Section 7 concludes the work by focusing

on the advantages and limitations of the proposed ap-

proach.

2 PROBLEM STATEMENT

In MDE context, one founding assumption is that au-

tomated model transformation reduces the cost and

time of development and facilitates the development

through code generation. Model transformations also

allow providing intermediate bridges between the dif-

ferent development phases of an application. Usually,

transformation programs express the automatic trans-

lation of a model to another model according to a set

of transformation rules. Writing these rules requires

on the one hand, to have a good knowledge about the

semantics of each input and output metamodels and

on the other hand, to have a good knowledge of a

speciﬁc language in order to implement these trans-

formation rules. In this paper, we are interested in

the challenge of deﬁning transformations with a min-

imum of domain knowledge. To address this chal-

lenge, we formulate the following research questions,

illustrated through the ﬁgure 1:

RQ1: Can we deﬁne the relationships between input

and output models using natural language?

RQ2: Can we generate the output models automati-

cally without using transformation rules that require

to use of a speciﬁc language?

RQ3: Can we use the natural language to deﬁne our

models or/and metamodels without using a speciﬁc

language in MDE context?

RQ4: Is there an optimal technique to model systems

without mastering a speciﬁc language or a modeling

tool?

When the model transformation is based on the spec-

iﬁcation of the correspondence rules between the el-

ements of the source and the target metamodels, the

domain experts have to implement it manually. To cir-

cumvent this burden, different research contributions

investigate the idea of deriving speciﬁcation or con-

crete transformation from example through ad-hoc or

machine learning algorithms. The proposed solutions

are generally known as Model Transformation By Ex-

ample (MTBE). MTBE approaches use with differ-

ent techniques such as genetic programming (Baki

and Sahraoui, 2016) and Ad-hoc Algorithm (Varr

2006).All these approaches attempt to generalize the

correspondence between source and target model el-

ements in the form of transformation rules written in

a speciﬁcation or implementation language. Learning

complex structure such as rules is, however, a com-

plex problem.

Another perspective on transformation automa-

tion is to reuse the large body of knowledge on natu-

ral language translation, which is a very similar prob-

lem. In particular, statistical machine translation can

be adapted to MTBE. This technique is based on the

statistical models whose parameters are derived us-

ing a parallel (or bilingual) corpus in the learning

phase. Generally, another corpus is also used, that

is known as monolingual corpus, to deﬁne the word

resemblance in target language (Koehn, 2009).

We believe that SMT is suitable to our problem

because the translation is implemented through the

statistical training using a set of examples without re-

quiring transformation languages.

3 MTBE USING SMT

Our objective is to deﬁne a transformation mecha-

nism that is based on a corpus of transformation ex-

amples(TE). Our approach can be divided into three-

steps: (1) data preparation to map transformation ex-

Model Transformation by Example with Statistical Machine Translation

amples into training data, (2) SMT training and (3) the

use of the trained SMT to actually transform models.

In the next, we explain these steps in detail.

3.1 Data Preparation

This step is a crucial part of our approach since

it allows to prepare the data that will be used to

train the statistical machine translation models. Data

preparation is done into two sub-steps: (1) trans-

forming source and target models into textual repre-

sentations, i.e., natural language sentences, and (2)

aligning source and target sentences that describe

semantically-equivalent source and target elements.

Figure 2 summarizes the data preparation step.

Figure 2: Corpus preparation process.

As in MDE, a model is an instance of its metamodel,

it can be decomposed into elements according to the

metamodel structure and constraints. Let us consider

the transformation problem of UML class diagrams

to relational schemas. For example, in a given UML

class diagram, one element can be a class ”Student”

with its attribute ”name”. For each element, we gen-

erate one or more sentences in a natural language.

Source models are translated into French, e.g., ”la

classe Student poss

ede attribut name”, which means

”class student has attribute name”. Target models

are translated into English. For example, in a rela-

tional schema corresponding to the considered class

diagram, we can ﬁnd a table ”Student” with a col-

umn ”name”. This model element will be translated

into English as ”Table Student has column name”.

The translation of model elements into natural lan-

guage sentences can be done manually, as it can

be automated as a simple model-to-text transforma-

tion.

Observation 1: Ambiguity Problem

To solve the problem of ambiguity, we have

proposed a parallel corpus for each transforma-

tion example by considering the source meta-

model as the source language and the target

metamodel as also the target language.

Once the source and the target models are translated

into natural language sentences, the next step con-

sists in aligning source and target sentences that de-

scribe semantically-equivalent elements. Here again,

the alignment can be automatic if the input and the

output models are created simultaneously and auto-

matically (in our case, this process is done by using

Acceleo language for following example). Otherwise,

it can be done manually.

3.2 SMT Training

After the data preparation, the set of all aligned sen-

tences of all the pair examples deﬁne the parallel cor-

pus, which will be used later to train the translation

model. Additionally, the sentences of all the target

model examples deﬁne the monolingual corpus that

will be used to derive the language model.

SMT training is based on the parallel and mono-

lingual corpora of the translation system to be used. In

the following, we explain the basic concepts of SMT

system and we present the translation and the lan-

guage models that are used in this step. Figure 4 de-

scribes the training process of our SMT system. The

ﬁrst model to train is the translation model. Accord-

ing to (Brown et al., 1993), a sentence f in a source

language has a possible translation to e in a target lan-

guage according to the probability P(e| f ). Starting

from the theorem of Bayes on the pair of sentences

(s,t), the probability P(e| f ) is computed according to

the following equation 1.

argmaxP(e| f ) = argmax

P( f |e).P(e)

P( f )

. (1)

The main objective of statistical translation is to ﬁnd

the best translation ˆe, i.e. the value that maximizes

P(e| f ). In addition, the probability P( f ) of the source

sentence f has no inﬂuence on the result of this equa-

tion, because it is ﬁxed. Sequentially, SMT transla-

tion process is deﬁned by this simpliﬁed equation 2

that maximizes the probability P(e| f ) according to re-

alized observations in a parallel corpus.

ˆe = argmaxP(e| f ) = argmaxP( f |e).P(e). (2)

MODELSWARD 2020 - 8th International Conference on Model-Driven Engineering and Software Development

Figure 3: Training process.

P( f |e) deﬁnes a translation model based on inter-

lingual probabilities and P(e) explains a language

model to evaluate the probability of a word-sequence.

This equation and the following ones are explained

through case study in Section 4. In the following, we

detail the translation and language models.

3.2.1 Translation Model

The translation model deﬁnes the probability that a

word or word-sequence in a source language will be

translated into one or more words in a target language.

This model compute the probability of a target sen-

tence e

= e

... e

to be associated to a sentence from

the source language f

= f

... f

. In our work, we ex-

perimented with IBM1 model. Its translation model

is deﬁned according to the following equation (Brown

et al., 1993).

P( f

) =

(I + 1)

∑

...

∑

∏

j=1

t( f

a j

). (3)

Where

∑

deﬁnes the possible alignment and

∏

j=1

t( f

a j

) explains the translation probability of f

to e

a j

apply to all words in the source sentence.

3.2.2 Language Model

The objective of SMT is not only to produce a word-

by-word translation sequence as output, but also to

guarantee the grammatical reasonableness of the re-

sults in the target language. To achieve this objective,

many researchers propose to adapt language model

for machine translation system. This model is deﬁned

by the following equation (Brown et al., 1993).

P(e

) ≈

∏

i=1

P(e

i−n+1

, ...e

i−1

). (4)

3.3 Model Transformation with SMT

In this sub-section, we describe our model transfor-

mation process using SMT system. Figure 4 gives

an overview of this process. Firstly, a model to be

transformed is translated into French sentences as de-

scribed in the data preparation process. Then, we use

the trained translation and language models to pro-

duce an equivalent English set of sentences describ-

ing the target model. Finally, this textual description

is mapper to a fully ﬂedged target model according to

the associated metamodel.

The SMT translation is performed in two steps: a

decoding phase and a sentence and a model genera-

tion phase.

Figure 4: Model transformation process with SMT system.

3.3.1 Decoder

The decoder is the main component of the SMT sys-

tem. It consists in providing an output text from a

source text based on translation and language models.

This treatment is deﬁned by the following equation

(Brown et al., 1993) and illustrated in Section 4:

ˆe = argmaxP( f

)P(e

) = P( f

, ˆa

| f

)P(e

). (5)

P( f

, ˆa

| f

) deﬁnes the probability of the best transla-

tion that is based on the highest alignment probability

(in equation 5 this alignment is presented by ˆa

| f

3.3.2 Sentence and Model Generation

After the decoding using word-by-word translation,

the sentence generator assemble the translated words

into sentences according to the language model. From

these sentences, we generate the target model accord-

ing to the data preparation process.

Model Transformation by Example with Statistical Machine Translation

4 ILLUSTRATIVE CASE STUDY

To better illustrate the proposed approach, we dis-

cuss UML class diagram to relational schema. This

case has been used in several papers in order to evalu-

ate model transformation approaches (see for example

(Baki and Sahraoui, 2016)).

Figure 5: Source and target metamodels.

4.1 Data Preparation

In this step, we are interested to deﬁne the parallel

and monolingual corpora in an automatic way by exe-

cuting a model-to-text transformations written in Ac-

celeo language. The generated sentences are apired

according to the paired model elements. Figure 6

shows an example of source-target model pair to-

gether with an excerpt of the paired sentences set.

Class 1

Class 2 Class 3

Attribut C1

Id_Class 2

Id_Class 3

Class 4

Id_Class 4

Attribut C4

1..*1..1

Class 2 Class 3

Id_2

Id_3

Class 4

Id_4

Attribut C4

1..*1..1

F 01

la classe 2

hérite de la classe

the table 2

contains

Attribut Id_2 de classe 2 a une valeur unique

Column Id_2 of Table 2 is a primary

………………………………………….

Class 6

Id_Class 6

Attribut C6

Class-Associa 5

Attribut C5

1..*

Class 6

Id_6

Attribut C6

Class-Associa 5

Attribut C5

Source Model

F 02

Classe_AssociationNM 5liée à

la classe

Table 5 has Foreign

primary

……………………………………..

1..*

Class 4

Id_Class 4

Attribut C4

Table 4Table 4

Id_Class 2Id_Class 2

Table 3Table 3

Id_Class 2Id_Class 2

Attribut C1

Table 2Table 2

Id_Class 2Id_Class 2

Attribut C1

Class 4

Attribut C4

Table 4Table 4

Id_4Id_4

Table 3Table 3

Id_3Id_3

Attribut C1

Table 2Table 2

Id_2Id_2

Attribut C1

hérite de la classe

contains

-all-columns of the table 1

Attribut Id_2 de classe 2 a une valeur unique

Column Id_2 of Table 2 is a primary

key

………………………………………….

Id_3Id_3

Id_Class 2Id_Class 2

Attribut C1

Table 6Table 6

Id_Class 2Id_Class 2

Attribut C1

Table 5Table 5

Id_6Id_6

Attribut C5

Id_4Id_4

Attribut C4

Table 6Table 6

Id_6Id_6

Attribut C6

Target Model

Classe_AssociationNM 5liée à

la classe

Table 5 has Foreign

-Key-which-is

primary

-key-of the table 6

……………………………………..

Id_2Id_2

Id_3Id_3

Figure 6: Data preparation process for UMLCD2RS.

4.2 SMT Training

In this step, we describe the computation process of

both translation and language models.

Translation Model. In general, the translation

model gives the probability of the word translation

from a source language to another word in a target

language. Based on this probability, we generate the

target words that will be used then as a data to cre-

ate the target model. In the training phase, we cal-

culate the parameters of Equation 1 that will be use-

ful in the test phase in order to compute the probabil-

ity of each word translation. The calculation of these

IBM1 model parameters is based on the Expectation-

Maximization (EM) algorithm. This algorithm allows

to maximize the reasonableness of the parameters to

be learned (Dempster et al., 1977). The following ta-

ble describes some values of translation table that de-

ﬁnes IBM1 parameters.

Table 1: Translation table values.

P(f|e) value P(f|e) value

P(attribut|column) 0.999 P(est|column) 0.007

P(association1n

|foreign-key)

0.499 P(poss

ede

|foreign-key)

1e-12

P(classe |has) 0.015 P(poss

ede |has) 0.999

P(valeur

|primary)

0.497 P(unique |key) 0.497

P(classe|table) 0.836 P(unique|table) 1e-12

P(la|has) 0.103 P(la|the) 0.999

Language Model. In SMT systems, the language

model is used to compute the probability of P(e)

for a word-sequence t in a target language that e =

, m

....m

. In our case, we used a bi-gram lan-

guage model that follows this probability (Brunning,

2010):

P(m

i−1

) =

c(m

i−1

)

c(m

i−1

)

(6)

Where c(m

i−1

) deﬁnes the number of this word se-

quence m

i−1

and c(m

i−1

) explains the number of

i−1

word. The following table describes the lan-

guage model values of UMLCD2RS example.

Table 2: Language model values.

P(m

| m

i−1

) value P(m

| m

i−1

) value

P(table|the) 0.992 P(there|the) 0.00

P(y|table) 0.349 P(column|table) 0.00

P(primary|a) 0.833 P(key|a) 0.00

P(has|x) 0.342 P(column|x) 0.00

P(has|primary) 0.000 P(key|primary) 0.97

P(table|the) 0.992 P(another-deﬁned|the) 0.00

4.3 Transformation with SMT

To illustrate our translation, we select a set of sen-

tences written in natural language that deﬁne class3,

class4 and relationship between them in the source

model of ﬁgure 6.

MODELSWARD 2020 - 8th International Conference on Model-Driven Engineering and Software Development

Table 3: Comparative study of model transformation techniques.

Authors Input Elements Meta-modeling Tools Used Method Output Elements

Our Paper Sentences/Models Natural language SMT system Sentences/Models

(Hammoudi et al., 2014) ST-Metamodels Ecore Language Matching tools mappings

(Berramla et al., 2017b) ST-Metamodels Ecore Language Matching Alg Tr rules(A/C)

(Berramla et al., 2017a) ST-Models Ecore Language FCA-Matching Tr rules (A/C)

(Kessentini et al., 2012) Bloks /// PSO and SA Models

Elt1: attribut Id

de classe 3 a une valeur unique.

Elt2: attribut Id

de classe 4 a une valeur unique.

Elt3: la classe 4 poss

ede attribut C4.

Elt4: la classe 4 poss

ede association1n avec la classe

Decoder. In this step, the decoder uses the param-

eters of IBM1 model

to ﬁnd the best translation of

source text from bilingual and monolingual corpora.

The calculated parameters are shown in table 1 and

2. Once previous tables (1 and 2) are created, the de-

coder can generate the target sentences from source

sentences based on the following equation.

ˆe = P( f

, ˆa

| f

)P(e

) =

∏

j=1

t( f

a j

)

∏

i=1

P(e

i−1

). (7)

IBM1 model is not based on the alignment where

P( f

, ˆa

| f

) =

t( f

a j

) that explains the best trans-

lation value which is selected from the previous table.

Sentence and Model Generation. Once the decod-

ing phase is done, the extraction of target sentences

can be executed based on this calculated probability

argmaxP( f

)P(e

). The output sentences of the

input sentences (see Figure 6 and its deﬁnition in nat-

ural language in this sub-section) are deﬁned as fol-

lows:

Translation of Elt1: column Id

of table 3 is a pri-

mary key.

Translation of Elt2: column Id

of table 4 is a pri-

mary key.

Translation of Elt3: the table 4 has column C4.

Translation of Elt4: the table 3 has foreign-key that-

is-deﬁned-as-primary-key-in the table 3. To have use-

ful MDE artifacts, it is necessary to map the sentences

generated by SMT system into models conform to re-

lational schema metamodel. In our case, we create

the models manually from their correspondence sen-

tences.

https://www.nltk.org/ modules/nltk/translate/ibm1.

html

5 EXPERIMENTATION AND

DISCUSSION

The objective of this section is to evaluate the pro-

posed approach with six transformation examples

from various domains. For each transformation we

collected/deﬁned a set of pairs of model examples.

The models were translated into natural language

texts. Table 4 shows the vocabulary volume (sen-

tences and words) for parallel corpus of each trans-

formation example (in our case, the monolingual cor-

pus size generally presents the half (1/2) of parallel

corpus for each example).

Table 4: Information about used transformation examples.

Transformation Examples Parallel Corpus

Example NO

Exples

Sents

Words

UMLCD2RS 10 154 1073

Book2Publication 10 66 688

Family2Person 10 88 1016

Ecore2Coq 10 392 3071

AADL2TA 05 118 1217

UMLSM2PetriNet 15 388 3899

We evaluate the precision and recall metrics as

adapted to model transformation in (Hammoudi et al.,

2014). Both metrics are calculated in terms of words

found by the automated SMT translation (W

auto

) w.r.t.

the words expected (W

expert

), i.e, deﬁned by the expert

who produced the model example pair. We deﬁne the

set W

positive

auto

∩W

expert

, as the set of words that

are obtained by SMT translation and are also included

in the expert’s words.

Figure 7: Precision and Recall of transformation examples.

Model Transformation by Example with Statistical Machine Translation

In this context, we applied the trained translation

model on the examples used for training. Figure 7

summarizes the results obtained for the precision and

recall. Our SMT system based on IBM1 model gives

perfect precision 100% for all transformation exam-

ples. For the recall our SMT system exhibits also per-

fect values 100% for all transformation examples ex-

cept for AADL2TA, which are considered as a very

less score in our SMT system (these values are de-

ﬁned between 88% and 93%).

These values reﬂect the difﬁculty of fully han-

dling transformation with complex semantic gaps be-

tween source and target models such as AADL2TA

example, but with well structured semantics such as

UMLCD2RS transformation we have obtained the

best values.

In our case, we generate the target models (in our

case, from target sentences are created the target mod-

els) without using a set of transformation rules but

only with a set of source and target model pairs. This

latter is used to specify the parallel and monolingual

corpora for our SMT system. In this case, we have

processed the following problems: RQ3 and RQ4 that

are presented in section 2 have been solved by speci-

fying the models in natural language using manual or

automatic preparation).

In model transformation context, all proposed

works are based on optimization technique or match-

ing tool in their main step, however,our approach

aims to ease the transformation by treating the chal-

lenge of RQ2 by using exactly SMT system to gen-

erate the output models without using a speciﬁc pro-

gram.

Observation 2: Using NL in MDE Context

The use of natural language facilitates meta-

modeling and model transformation processes.

These latter are done automatically without ma-

nipulating speciﬁc languages or tools.

Our approach makes it possible to solve the problem

RQ1 focusing on learning system based on parallel

and monolingual corpora using IBM1 model. In this

case, parallel and monolingual corpora are proposed

as a deﬁnition of transformation rules and the execu-

tion of IBM1 is deﬁned as the executable of transfor-

mation rules.

6 RELATED WORK

The model transformation challenge is still how to

automate the model transformation process. Several

studies suggested to interpret this challenge into two

principal classes according to the type of input ele-

ment: the ﬁrst class is based on metamodels and the

second one manipulates models to automate or semi-

automate transformation process.

First class is focused on the detection of relation-

ships between the input and the output metamodels

automatically. It is important to notify that some

works are focused on specifying semi-automatically

a transformation rules by using matching techniques.

For instance, this work (Hammoudi et al., 2014) de-

scribes the use of various matching tools and tech-

niques to automate model transformations but these

matching techniques are based also on metamodel

pretreatment and require understanding the basic in-

formation about the ontologies. Another example

that illustrates this idea is Berramla’s paper (Berramla

et al., 2017b) that is focused on matching algorithm

but it compares all metamodel elements without con-

sidering their types and uses metamodels as input data

without changing their structure. Both abstract and

concrete transformation rules are generated from sim-

ple mapping elements, however, the intervention of an

expert is obligatory for complex transformation ex-

amples to validate their generated mappings.

Second class is founded on the use of models

as input elements to automate model transformation.

This category contains also several proposed works

using different techniques. In what follows, we brieﬂy

discuss on these prpoposed works. Some works are

based on MTBE using ad-hoc algorithms. One among

the ﬁrst researches in model transformation by exam-

ple is the Varr

o ’s work (Varr

o, 2006) that uses as in-

put elements a set of source and target model pairs and

the prototype mapping between each model pair in or-

der to help the transformation designer the creation

of model transformation semi-automatically. Other

studies use artiﬁcial intelligence (AI) techniques. In

(Baki and Sahraoui, 2016) is based on genetic pro-

gramming to learn operational rules from source and

target model pairs and a set of transformation rules

by adding also the control of each rule during the their

generation. In (Kessentini et al., 2012) describe a new

approach to deﬁne how to integrate Particle Swarm

Optimization (PSO) and Simulated Annealing (SA)

in order to generate the output models automatically

without using a transformation program. The rest of

these studies integrate the mathematical methods to

deﬁne the model transformation by examples. For in-

stance, in (Berramla et al., 2017a) a new approach

is proposed deﬁning the hybridization between the

model-matching and the formal concept analysis. The

ﬁrst one is used to create the correspondences from

Transformation rules written in abstract level(or and in

concrete levels).

MODELSWARD 2020 - 8th International Conference on Model-Driven Engineering and Software Development

source and target models and the second one is ap-

plied to reduce the redundancy of transformation rule

creation.

From the table 3, we note that all proposed ap-

proaches are based on metamodeling tools or lan-

guages to have a best representation of their input el-

ements which require a little times and efforts to in-

terpret their input elements but with our approach, we

use only the natural language to describe the models

to be translated.

The main challenge of the most proposed works

in this context is how to create transformation rules

semi-automatically. This paper is focused on how

to translate easily a model to another model without

using tools or languages in both metamodeling and

transformation levels.

7 CONCLUSION AND FUTURE

WORK

During recent years, model transformation by ex-

ample has seen in several works such as (Baki and

Sahraoui, 2016; Varr

o, 2006) to deﬁne the model

transformation in a semi-automatic way but applying

the proposed techniques are limited to simple trans-

formation examples. In this paper, we proposed an

approach to deﬁne the model transformation automat-

ically using only a set of models and SMT system

more speciﬁcally IBM1 model in order to reduce the

costs and the time of software development.

The most important objective of this work is not

only to automate transformation process but also to

facilitate the modeling phase by using natural lan-

guage without basing on speciﬁc tools or/and lan-

guages that require good knowledge about them.

Once the modeling phase is executed, a set of sen-

tences written in two languages are builded from the

source and the target model pairs. From these sen-

tences, is established a parallel corpus which is useful

in the training phase to calculate a set of parameters

that has been also used in the test phase in order to

obtain a good translation with this system. Finally the

translated sentences permit to create automatically the

target models through the use of their metamodels.

This process changes from one transformation exam-

ple to an another according to the structure of target

metamodel.

Improvements can be planned as perspectives for

this work. First of all, to propose a generalization of

the transformation process that will make it possible

to translate any model into a set of sentences written

in natural language. Also, we can use other transla-

tion system such as phrase-based translation system.

ACKNOWLEDGEMENTS

This work has been funded in part by the europian

project PRIMA WaterMed 4.0, ”Efﬁcient use and

management of conventional and non-conventional

water resources through smart technologies applied

to improve the quality and safety of Mediterranean

agriculture in semi-arid areas” and by MESRS, ”Min-

ist

ere de l’enseignement sup

erieur et de la recherche

scientiﬁque”.

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