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”.
REFERENCES
Al-Ghawanmeh, F. and Sma
¨
ıli, K. (2017). Statistical ma-
chine translation from arab vocal improvisation to
instrumental melodic accompaniment. Journal of
the International Science and General Applications,
1(1):11–17.
Baki, I. and Sahraoui, H. (2016). Multi-step learning and
adaptive search for learning complex model transfor-
mations from examples. ACM Trans. Softw. Eng.
Methodol., 25(3):20:1–20:37.
Berramla, K., Deba, E. A., Benyamina, A., Touam, R.,
Brahimi, Y., and Benhamamouch, D. (2017a). Formal
concept analysis for speciﬁcation of model transfor-
mations. In Embedded & Distributed Systems (EDiS),
pages 1–6. IEEE.
Berramla, K., Deba, E. A., Benyamina, A. E. H., and Ben-
hamamouch, D. (2017b). A Contribution to the Spec-
iﬁcation of Model Transformations with Metamodel
Matching Approach. IJISMD, 8(3):1–23.
Brown, P. F., Pietra, V. J. D., Pietra, S. A. D., and Mercer,
R. L. (1993). The mathematics of statistical machine
translation: Parameter estimation. Computational lin-
guistics, 19(2):263–311.
Brunning, J. J. J. (2010). Alignment models and algorithms
for statistical machine translation. PhD thesis, Uni-
versity of Cambridge.
Dempster, A. P., Laird, N. M., and Rubin, D. B. (1977).
Maximum likelihood from incomplete data via the em
algorithm. Journal of the royal statistical society. Se-
ries B (methodological), pages 1–38.
Hammoudi, S., Feki, J., and Laﬁ, L. (2014). Metamodel
matching techniques: Review, comparison and eval-
uation. International Journal of Information System
Modeling and Design, pages 1–27.
Kessentini, M., Sahraoui, H., Boukadoum, M., and Omar,
O. B. (2012). Search-based model transformation by
example. Software & Systems Modeling, 11(2):209–
226.
Kleppe, A. G., Warmer, J., Warmer, J. B., and Bast, W.
(2003). MDA explained: the model driven architec-
ture: practice and promise. Addison-Wesley Profes-
sional.
Koehn, P. (2009). Statistical machine translation. Cam-
bridge University Press.
Varr
´
o, D. (2006). Model transformation by example. In In-
ternational Conference on Model Driven Engineering
Languages and Systems, pages 410–424. Springer.