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Detecting complex c...
Detecting complex changes and refactorings during (Meta)model evolution
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- Khelladi, Djamel Eddine (author)
- Sorbonne Université,Sorbonne University
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- Hebig, Regina (author)
- Gothenburg University,Göteborgs universitet,Institutionen för data- och informationsteknik (GU),Department of Computer Science and Engineering (GU),University of Gothenburg
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- Bendraou, Reda (author)
- Sorbonne Université,Sorbonne University
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- Robin, Jacques (author)
- Sorbonne Université,Sorbonne University
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- Gervais, Marie-Pierre (author)
- Université Paris Ouest Nanterre X,Paris West Nanterre University X
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(creator_code:org_t)
- Elsevier BV, 2016
- 2016
- English.
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In: Elsevier - Information Systems. - : Elsevier BV. - 0306-4379. ; 62, s. 220-241
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Abstract
Subject headings
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- Evolution of metamodels can be represented at the finest grain by the trace of atomic changes such as add, delete, and update of elements. For many applications, like automatic correction of models when the metamodel evolves, a higher grained trace must be inferred, composed of complex changes, each one aggregating several atomic changes. Complex change detection is a challenging task since multiple sequences of atomic changes may define a single user intention and complex changes may overlap over the atomic change trace. In this paper, we propose a detection engine of complex changes that simultaneously addresses these two challenges of variability and overlap. We introduce three ranking heuristics to help users to decide which overlapping complex changes are likely to be correct. In our approach, we record the trace of atomic changes rather than computing them with the difference between the original and evolved metamodel. Thus, we have a complete and an ordered sequence of atomic changes without hidden changes. Furthermore, we consider the issue of undo operations (i.e. change canceling actions) while recording the sequence of atomic changes, and we illustrate how we cope with it. We validate our approach on 8 real case studies demonstrating its feasibility and its applicability. We observe that a full recall is always reached in all case studies and an average precision of 70.75%. The precision is improved by the heuristics up to 91% and 100% in some cases.
Subject headings
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik -- Datorsystem (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering -- Computer Systems (hsv//eng)
- NATURVETENSKAP -- Data- och informationsvetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences (hsv//eng)
Keyword
- Metamodel
- Evolution
- Complex change
- Refactoring
- Detection
- Evolution
Publication and Content Type
- ref (subject category)
- art (subject category)
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