Reduction of Multiple Move Method Suggestions Using Total Call-Frequencies of Distinct Entities

Reduction of Multiple Move Method Suggestions Using Total Call-Frequencies of Distinct Entities

Автор: Atish Kumar Dipongkor, Rayhanul Islam, Nadia Nahar, Iftekhar Ahmed, Kishan Kumar Ganguly, S.M. Arif Raian, Abdus Satter

Журнал: International Journal of Information Engineering and Electronic Business @ijieeb

Статья в выпуске: 4 vol.12, 2020 года.

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Inappropriate placement of methods causes Feature Envy (FE) code smell and makes classes coupled with each other. To achieve cohesion among classes, FE code smell can be removed using automated Move Method Refactoring (MMR) suggestions. However, challenges arise when existing techniques provide multiple MMR suggestions for a single FE instance. The developers need to manually find an appropriate target classes for applying MMR as an FE instance cannot be moved to multiple classes. In this paper, a technique is proposed named MultiMMRSReducer, to reduce multiple MMR suggestions by considering the Total Call-Frequencies of Distinct Entities (TCFDE). Experimental results show that TCFDE can reduce the multiple MMR suggestions of an FE instance and performs 77.92% better than an existing approach, namely, JDeodorant. Moreover, it can ensure minimum future changes in the dependent classes of an FE instance.

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Feature Envy, Move Method Refactoring

Короткий адрес: https://sciup.org/15017409

IDR: 15017409   |   DOI: 10.5815/ijieeb.2020.04.03

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