Anti‐bloater class restructuring: An exploratory study. Issue 3 (9th February 2022)

• Record Type: Journal Article
• Authors: Machado, João Paulo L.; Paula‐Sobrinho, Elder V. P.; Maia, Marcelo A.
• Is Part Of: Journal of software. Volume 34:Issue 3(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-02-09
• Subjects: bloaters -- predictive models -- refactoring -- structure -- temporal analysis; Software engineering -- Periodicals; Computer software -- Development -- Periodicals; Software maintenance -- Periodicals; 005.1
• DOI: 10.1002/smr.2431
• Languages: English
• ISSNs: 2047-7473

Abstract

Abstract: Proper software modularization still poses challenges to developers. One of the symptoms of inappropriate modularization is the large size of object‐oriented classes. In that case, a possible solution would be class restructuring with refactorings, such as Extract Class, Extract Super‐Class, or Move Method . However, class refactoring is challenging because of the possible side effects of improper changes. In this context, more effective decision support systems on which classes are worthwhile for restructuring to improve modularity are still lacking. This work focuses on exploring possible alternatives for supporting decision on class restructuring. A prospective study was performed on selected kinds of restructuring, aiming at determining what types of strategies are typically adopted to restructure bloated classes and which classes developers decided to restructure. Then, we proposed and evaluated a predictive model for indicating which classes to restructure, aiming at delivering a restructuring guide on those classes. Finally, we conducted a qualitative study to evaluate the perception of developers on such guides based on predictions for real software. The results have shown situations in which the proposed predictions could help the restructuring process but also elucidated possible improvements and limitations. Abstract :