A systematic review on search-based refactoring

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@Article{Mariani:2017:IST,
  author =       "Thaina Mariani and Silvia Regina Vergilio",
  title =        "A systematic review on search-based refactoring",
  journal =      "Information and Software Technology",
  year =         "2017",
  volume =       "83",
  pages =        "14--34",
  month =        mar,
  keywords =     "genetic algorithms, genetic programming, SBSE,
                 Search-based software engineering, Refactoring,
                 Evolutionary algorithms",
  ISSN =         "0950-5849",
  DOI =          "doi:10.1016/j.infsof.2016.11.009",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0950584916303779",
  size =         "21 pages",
  abstract =     "To find the best sequence of refactorings to be
                 applied in a software artefact is an optimization
                 problem that can be solved using search techniques, in
                 the field called Search-Based Refactoring (SBR). Over
                 the last years, the field has gained importance, and
                 many SBR approaches have appeared, arousing research
                 interest.

                 Objective: The objective of this paper is to provide an
                 overview of existing SBR approaches, by presenting
                 their common characteristics, and to identify trends
                 and research opportunities. Method: A systematic review
                 was conducted following a plan that includes the
                 definition of research questions, selection criteria, a
                 search string, and selection of search engines. 71
                 primary studies were selected, published in the last
                 sixteen years. They were classified considering
                 dimensions related to the main SBR elements, such as
                 addressed artefacts, encoding, search technique, used
                 metrics, available tools, and conducted
                 evaluation.

                 Results: Some results show that code is the most
                 addressed artifact, and evolutionary algorithms are the
                 most employed search technique. Furthermore, most
                 times, the generated solution is a sequence of
                 refactorings. In this respect, the refactorings
                 considered are usually the ones of the Fowler's
                 Catalogue. Some trends and opportunities for future
                 research include the use of models as artefacts, the
                 use of many objectives, the study of the bad smells
                 effect, and the use of hyper-heuristics.

                 Conclusions: We have found many SBR approaches, most of
                 them published recently. The approaches are presented,
                 analysed, and grouped following a classification
                 scheme. The paper contributes to the SBR field as we
                 identify a range of possibilities that serve as a basis
                 to motivate future researches.",
  notes =        "Brief mention of GP. Cites \cite{Jensen:2010:gecco},
                 \cite{langdon:2009:gecco3}",
}

Genetic Programming entries for Thaina Mariani Silvia Regina Vergilio

Citations