Model refactoring by example: A multi‐objective search based software engineering approach. Issue 4 (28th November 2017)

• Record Type: Journal Article
• Authors: Ghannem, Adnane; Kessentini, Marouane; Hamdi, Mohammad Salah; El Boussaidi, Ghizlane
• Is Part Of: Journal of software. Volume 30:Issue 4(2018)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2017-11-28
• Subjects: model evolution -- NSGA‐II -- Pareto front -- refactoring by example -- software maintenance; Software engineering -- Periodicals; Computer software -- Development -- Periodicals; Software maintenance -- Periodicals; 005.1
• DOI: 10.1002/smr.1916
• Languages: English
• ISSNs: 2047-7473

Abstract

Abstract: Declarative rules are frequently used in model refactoring in order to detect refactoring opportunities and to apply the appropriate ones. However, a large number of rules is required to obtain a complete specification of refactoring opportunities. Companies usually have accumulated examples of refactorings from past maintenance experiences. Based on these observations, we consider the model refactoring problem as a multi objective problem by suggesting refactoring sequences that aim to maximize both structural and textual similarity between a given model (the model to be refactored) and a set of poorly designed models in the base of examples (models that have undergone some refactorings) and minimize the structural similarity between a given model and a set of well‐designed models in the base of examples (models that do not need any refactoring). To this end, we use the Non‐dominated Sorting Genetic Algorithm (NSGA‐II) to find a set of representative Pareto optimal solutions that present the best trade‐off between structural and textual similarities of models. The validation results, based on 8 real world models taken from open‐source projects, confirm the effectiveness of our approach, yielding refactoring recommendations with an average correctness of over 80%. In addition, our approach outperforms 5 of the state‐of‐the‐art refactoring approaches.