Reducing interactive refactoring effort via clustering-based multi-objective search

Abstract

Refactoring is nowadays widely adopted in the industry because bad design decisions can be very costly and extremely risky. On the one hand, automated refactoring does not always lead to the desired design. On the other hand, manual refactoring is error-prone, time-consuming and not practical for radical changes. Thus, recent research trends in the field focused on integrating developers feedback into automated refactoring recommendations because developers understand the problem domain intuitively and may have a clear target design in mind. However, this interactive process can be repetitive, expensive, and tedious since developers must evaluate recommended refactorings, and adapt them to the targeted design especially in large systems where the number of possible strategies can grow exponentially.

BibTeX

@inproceedings{Alizadeh-Kessentini:ASE18,
  author    = {Vahid Alizadeh and
               Marouane Kessentini},
  title     = {Reducing interactive refactoring effort via clustering-based multi-objective search},
  booktitle = {ASE},
  pages     = {464--474},
  publisher = {{ACM}},
  year      = {2018},
}

Reducing interactive refactoring effort via clustering-based multi-objective search

Abstract

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