Motivation
Refactoring has emerged as a successful technique to enhance the internal structure of software by a series of small, behaviour-preserving transformations [4]. However, due to complex dependencies and conflicts between the individual refactorings, it is difficult to choose the best sequence of refactoring steps in order to effect a specific improvement. In the case of large systems the situation becomes acute because existing tools offer only limited support for their automated application [8]. Therefore, search-based approaches have been suggested in order to provide automation in discovering appropriate refactoring sequences [6,11]. The idea is to see the design process as a combinatorial optimization problem, attempting to derive the best solution (with respect to a quality measure called objective function) from a given initial design [9].
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Qayum, F. (2010). Automated Assistance for Search-Based Refactoring Using Unfolding of Graph Transformation Systems. In: Ehrig, H., Rensink, A., Rozenberg, G., Schürr, A. (eds) Graph Transformations. ICGT 2010. Lecture Notes in Computer Science, vol 6372. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15928-2_34
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