New approach for model merging and transformation

Thakare, Bhushan S.; Dube, Mahesh R.

doi:10.1109/iccci.2012.6158834

Cited by 2 publications

(9 citation statements)

References 8 publications

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“…Each merge is distinguished from the others by how it combines the elements of the input models. For example, RM1 which contains two elements designated respectively (1, 2) and (3,4) is obtained by combining element 1 with element 2, and element 3 with element 4, while RM2 is obtained by combining element 1 with element 3, and element 2 with element 4. The sub-elements that appear in blue in Fig.…”

Section: Motivating Examplementioning

confidence: 99%

“…In the first step, we merge M1 with M3 by combining the elements 1 and 4. This produces a model with one element (1,4). Finally, we merge the resulting model with M2 by combining the elements (1, 4) with 3 to obtain RM4.…”

Section: Motivating Examplementioning

confidence: 99%

“…In the same way, we calculate the similarity degree for the criterion similarity of class names, which is equal to 0.22. Finally, using the criteria weights of Table 1 and the formula 3, we obtain the similarity degree of the tuple (1,3,4), which is equal to 0.31.…”

Section: Similarity Degree Of Tuplesmentioning

confidence: 99%

“…The combination aims also to carry out diverse types of analysis like synthesis, verification, and validation [2] [3]. Model merging is usually used to resolve conflicts that occur when integrating independently developed modules into a single project [4] [5], but it is also used in diverse other domains. For example, model merging is used to build a global ontology from a set of local ones in the ontology research field [6], and to merge conceptual database schemas in the database field [7].…”

Section: Introductionmentioning

confidence: 99%

“…In Software Product Line Engineering (SPLE) [8], model merging is considered as a fundamental step when a set of similar product variants need to be migrated into a model-based Software Product Line (SPL) [9]. In the literature, model merging is defined as the process of combining information from two or more models into a single one [4] [9], and it is commonly implemented through the three following operators: compare, match and merge [10] [11]. The first and the second operators are used to detect relationships between models, while the third one is used to combine them into a single model [12].…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

An Approach and a Tool for Merging a Set of Models in Pairwise Way

Boubakir

Chaoui

2021

MJCS

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Model merging addresses the problem of combining information from a set of models into a single one. This task is considered to be an important step in various software engineering practices. When many (more than two) models need to be merged, the most practiced technique is to perform the merge in a pairwise way, without considering the order of merging. The problem with this technique is that the resulting quality is not guaranteed because it is influenced by such an order. In this paper, we propose a pairwise approach for model merging aiming to provide better results by taking into account the order of merging. This approach proposes to combine models in an iterative process until obtaining only one model. At each iteration, we first compare each pair of models in order to measure the similarity between them and to identify the correspondences between their component elements. This is performed using two heuristic-based operators respectively named compare and match. After that, we identify the most similar pairs of models and merge them using a proposed operator. We have implemented our approach as tool support called 3M and evaluated it on a set of case studies.

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Section: Motivating Examplementioning

confidence: 99%

Section: Motivating Examplementioning

confidence: 99%