A survey on search-based model-driven engineering

Boussaïd, Ilhem; Siarry, Patrick; Mohamed, Azah

doi:10.1007/s10515-017-0215-4

Cited by 68 publications

(46 citation statements)

References 174 publications

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“…We compare our SPARQL to Hive system with Jena by always using the three datasets LUBM1, LUBM2, LUBM5, generally on the majority of the queries; SPARQL2hive is more powerful than Jena at the runtime of LUBM Benchmark queries. Figures 7,8,9,10,11,12,13,14,15,and 16 show the results of this comparison for all LUBM queries.…”

Section: Discussionmentioning

confidence: 99%

“…The meta-model has the particularity of containing all the concepts necessary to create models in a domain, a particular context: the meta-model is at the heart of the MDE. More precisely, the role of a meta-model is to define, as a minimum, the abstract syntax of a formalism, by defining concepts and relations between them [13].…”

Section: Model Driven Engineeringmentioning

confidence: 99%

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New Approach based on Model Driven Engineering for Processing Complex SPARQL Queries on Hive

Banane¹,

Belangour²

2019

IJACSA

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Semantic web technologies are increasingly used in different domains. The core technology of the Semantic Web is the RDF standard. Today with the growth of RDF data it requires systems capable of handling these large volumes of data and responding to very complex requests at the join level, Several RDF data processing systems have been proposed, but are not dedicated to handling complex SPARQL queries. in this paper we present a new approach based on model driven engineering for processing complex SPARQL queries using one of the big data processing tools named Hive. We evaluate our system using three datasets from LUBM Benchmark. The results of this evaluation show the performance, and the scalability of our approach, also give very interesting results when it is compared with existing works.

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Section: Discussionmentioning

confidence: 99%

Section: Model Driven Engineeringmentioning

confidence: 99%

New Approach based on Model Driven Engineering for Processing Complex SPARQL Queries on Hive

Banane¹,

Belangour²

2019

IJACSA

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“…Over the years SBSE has been applied to various fields of software engineering for example, requirements [16,109], automatic program repair [52], Software Product Lines [15,31,83], Performance configuration optimization [32,66,67,69,72] to name of few. SBSE has been applied to other fields and has their own surveys such as design [79], model-driven engineering [12], genetic improvement of programs [76], refactoring [56], Testing [46,86] as well as more general surveys [18,34]. Figure 2 provides a (very) short tutorial on SBSE and Figure 3 characterizes some of the differences between MSR and SBSE.…”

Section: What? (Definitions)mentioning

confidence: 99%

Data-driven search-based software engineering

Nair

Agrawal

Chen

et al. 2018

Proceedings of the 15th International Conference on Mining Software Repositories

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This paper introduces Data-Driven Search-based Software Engineering (DSE), which combines insights from Mining Software Repositories (MSR) and Search-based Software Engineering (SBSE). While MSR formulates software engineering problems as data mining problems, SBSE reformulate Software Engineering (SE) problems as optimization problems and use meta-heuristic algorithms to solve them. Both MSR and SBSE share the common goal of providing insights to improve software engineering. The algorithms used in these two areas also have intrinsic relationships. We, therefore, argue that combining these two fields is useful for situations (a) which require learning from a large data source or (b) when optimizers need to know the lay of the land to find better solutions, faster.This paper aims to answer the following three questions: (1) What are the various topics addressed by DSE?, (2) What types of data are used by the researchers in this area?, and (3) What research approaches do researchers use? The paper briefly sets out to act as a practical guide to develop new DSE techniques and also to serve as a teaching resource.This paper also presents a resource (tiny.cc/data-se) for exploring DSE. The resource contains 89 artifacts which are related to DSE, divided into 13 groups such as requirements engineering, software product lines, software processes. All the materials in this repository have been used in recent software engineering papers; i.e., for all this material, there exist baseline results against which researchers can comparatively assess their new ideas.

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“…Recently, research combining SBSE and MDE for a range of purposes has become increasingly popular. The term search-based model-driven engineering (SBMDE, [BSAN17]) has been proposed as an umbrella term for these efforts. One particular line of research in SBMDE, which we call model-driven optimization (MDO), aims to reduce the level of expertise required by users of SBSE techniques 1 .…”

Section: Introductionmentioning

confidence: 99%

“…Recently, a variety of MDO frameworks has emerged [BFT + 17, AVS + 14, ZM16,Str17] and been applied successfully in numerous use-cases, including security-oriented software refactoring [RKL + 18], model generation [SNV18], transformation modularization [FTK + 17], and various more examples [BSAN17]. A key distinction in MDO frameworks concerns the way in which solutions are encoded [ZM16]: The model-based encoding approach represents solutions as models.…”

Section: Introductionmentioning

confidence: 99%

Searching for Optimal Models: Comparing Two Encoding Approaches.

John¹,

Burdusel²,

Bill³

et al. 2019

JOT

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Search-Based Software Engineering (SBSE) is about solving software development problems by formulating them as optimization problems. In the last years, combining SBSE and Model-Driven Engineering (MDE), where models and model transformations are treated as key artifacts in the development of complex systems, has become increasingly popular. While search-based techniques have often successfully been applied to tackle MDE problems, a recent line of research investigates how a model-driven design can make optimization more easily accessible to a wider audience. In previous model-driven optimization efforts, a major design decision concerns the way in which solutions are encoded. Two main options have been explored: a model-based encoding representing candidate solutions as models, and a rule-based encoding representing them as sequences of transformation rule applications. While both encodings have been applied to different use cases, no study has yet compared them systematically. To close this gap, we evaluate both approaches on a common set of optimization problems, investigating their impact on the optimization performance. Additionally, we discuss their differences, strengths, and weaknesses laying the foundation for a knowledgeable choice of the right encoding for the right problem.

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A survey on search-based model-driven engineering

Cited by 68 publications

References 174 publications

New Approach based on Model Driven Engineering for Processing Complex SPARQL Queries on Hive

New Approach based on Model Driven Engineering for Processing Complex SPARQL Queries on Hive

Data-driven search-based software engineering

Searching for Optimal Models: Comparing Two Encoding Approaches.

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