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A survey on search-based model-driven engineering

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Abstract

Model-driven engineering (MDE) and search-based software engineering (SBSE) are both relevant approaches to software engineering. MDE aims to raise the level of abstraction in order to cope with the complexity of software systems, while SBSE involves the application of metaheuristic search techniques to complex software engineering problems, reformulating engineering tasks as optimization problems. The purpose of this paper is to survey the relatively recent research activity lying at the interface between these two fields, an area that has come to be known as search-based model-driven engineering. We begin with an introduction to MDE, the concepts of models, of metamodels and of model transformations. We also give a brief introduction to SBSE and metaheuristics. Then, we survey the current research work centered around the combination of search-based techniques and MDE. The literature survey is accompanied by the presentation of references for further details.

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Notes

  1. www.omg.org.

  2. http://www.eclipse.org/modeling/.

  3. The model-driven architecture approach defines system functionality using a platform-independent model (PIM) which exhibits a sufficient degree of independence so as to enable its mapping to one or more platforms. This is commonly achieved by defining a set of services in a way that abstracts out technical details. A platform-specific model (PSM) combines the specifications in the PIM with the details required to stipulate how a system uses a particular type of platform (Truyen 2006).

  4. The term “search” is used to refer to the metaheuristic search-based optimization techniques.

  5. http://crestweb.cs.ucl.ac.uk/resources/sbse_repository/repository.html.

  6. Various refactoring approaches are discussed in Räihä (2010) (14 papers). The approaches presented in this survey investigate refactoring at the design level (6 papers). Some other search-based refactoring approaches at the code level have been cited but not discussed (17 papers).

  7. http://martin-fleck.github.io/momot/.

  8. Models must be smaller and simpler (more abstract) than the SUT, or at least easier to check, modify, and maintain. Otherwise, the efforts of validating the model would exactly match the efforts of validating the SUT. On the other hand, models must be precise and complete enough to allow automated derivation of tests.

  9. The generated test cases are abstract test cases as the model which is given as input to test generation process is the abstract representation of the SUT.

  10. The work of Herrmannsdoerfer et al. (2009) is concerned with the migration of models in response to metamodel adaptation. The approach presented defines a set of evolutionary operators on metamodels. The authors associate a model migration strategy to each operator. Consequently, if a developer modifies his/her metamodel by using the provided operators, the corresponding migration can be carried out automatically.

  11. Genetic Programming as a means of program improvement has come to be known as genetic improvement and has also been referred to as evolutionary improvement.

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The authors thank the reviewers for their insightful and constructive comments and suggestions.

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  1. LSI, University of Science and Technology Houari Boumediene, BP n, El-Alia, 16111, Bab-Ezzouar, Algiers, Algeria

    Ilhem Boussaïd & Mohamed Ahmed-Nacer

  2. LiSSi (EA 3956), Université de Paris-Est Créteil Val de Marne, 61 avenue du Général de Gaulle, 94010, Créteil, France

    Patrick Siarry

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Boussaïd, I., Siarry, P. & Ahmed-Nacer, M. A survey on search-based model-driven engineering. Autom Softw Eng 24, 233–294 (2017). https://doi.org/10.1007/s10515-017-0215-4

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