Using logic programming for adapting models to metamodel evolution

Anguel, Fouzia; Amirat, Abdelkrim; Bounour, Nora

doi:10.1504/ijiids.2017.086194

Cited by 1 publication

(1 citation statement)

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“…Difference engines may make mistakes by displaying false-negative or false-positive correspondences, as we have previously seen it in this paper and as confirmed in [41] . After the customization of difference results and for reusability goals, many works tackled the process of capturing and constructing evolution operators [42][43][44][45][46] . For instance, authors in [2] have defined a catalog of 61 reusable evolution operators (30 are atomic and 31 are composite) with the aim of treating the coupledevolution of meta-models (M2) and models (M1).…”

Section: Evolution Operatorsmentioning

confidence: 99%

A scalable model based approach for data model evolution: Application to space missions data models

Oubelli

Aït‐Ameur

Bedouet

et al. 2018

Computer Languages, Systems & Structures

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Open Archive Toulouse Archive OuverteOATAO is an open access repository that collects the work of Toulouse researchers and makes it freely available over the web where possible a b s t r a c t During the development of a complex system, data models are the key to a successful engineering process, as they contain and organize all the information manipulated by the different functions involved in the design of the system. Moreover, these data models evolve throughout the design, as the development raises issues that have to be solved through a restructuration of data organization. But any such data model evolution has a deep impact on the functions that have already being defined.Recent research tries to deal with this issue by studying how complex industrial data models evolve from one version to another and how their data instances co-evolve. Complexity and scalability issues make this problem a major scientific challenge, leading to huge gains in development efficiency. This problem is of particular interest in the field of aeronautics and space systems. Indeed, the development of these systems produces many complex data models associated to the designed systems and/or to the systems under design, hence on the one hand data models are available. On the other hand, it is well known that these systems are developed in the context of collaborative projects that may last for decades. In such projects, specifications together with the associated data models are bound to evolve and engineering processes shall take into account this evolution.Our work addresses the problem of data model evolution in a model-driven engineering setting. We focus on minimizing the impact of model evolution on the system development processes in the specific context on the space engineering area, where data models may involve thousands of concepts and relationships, and we investigate the performance of the model-based development (MBD) approach we propose for data model evolution over two space missions, namely PHARAO and MICROSCOPE.

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