Evaluation of move method refactorings recommendation algorithms: are we doing it right?

Evgenii Novozhilov, Ivan Veselov, Michail Pravilov, Timofey Bryksin

Research outputpeer-review

Abstract

Previous studies introduced various techniques for detecting Move Method refactoring opportunities. However, different authors have different evaluations, which leads to the fact that results reported by different papers do not correlate with each other and it is almost impossible to understand which algorithm works better in practice. In this paper, we provide an overview of existing evaluation approaches for Move Method refactoring recommendation algorithms, as well as discuss their advantages and disadvantages. We propose a tool that can be used for generating large synthetic datasets suitable for both algorithms evaluation and building complex machine learning models for Move Method refactoring recommendation
Original languageEnglish
Title of host publicationProceedings of the 3rd International Workshop on Refactoring
Publication statusPublished - May 2019
Event3rd International Workshop on Refactoring - Montreal, Quebec
Duration: 28 May 201928 May 2019

Conference

Conference3rd International Workshop on Refactoring
Abbreviated titleIWOR '19
CountryCanada
CityMontreal, Quebec
Period28/05/1928/05/19

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Cite this

Novozhilov, E., Veselov, I., Pravilov, M., & Bryksin, T. (2019). Evaluation of move method refactorings recommendation algorithms: are we doing it right? In Proceedings of the 3rd International Workshop on Refactoring
Novozhilov, Evgenii ; Veselov, Ivan ; Pravilov, Michail ; Bryksin, Timofey . / Evaluation of move method refactorings recommendation algorithms: are we doing it right?. Proceedings of the 3rd International Workshop on Refactoring. 2019.
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title = "Evaluation of move method refactorings recommendation algorithms: are we doing it right?",
abstract = "Previous studies introduced various techniques for detecting Move Method refactoring opportunities. However, different authors have different evaluations, which leads to the fact that results reported by different papers do not correlate with each other and it is almost impossible to understand which algorithm works better in practice. In this paper, we provide an overview of existing evaluation approaches for Move Method refactoring recommendation algorithms, as well as discuss their advantages and disadvantages. We propose a tool that can be used for generating large synthetic datasets suitable for both algorithms evaluation and building complex machine learning models for Move Method refactoring recommendation",
author = "Evgenii Novozhilov and Ivan Veselov and Michail Pravilov and Timofey Bryksin",
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Novozhilov, E, Veselov, I, Pravilov, M & Bryksin, T 2019, Evaluation of move method refactorings recommendation algorithms: are we doing it right? in Proceedings of the 3rd International Workshop on Refactoring., Montreal, Quebec, 28/05/19.

Evaluation of move method refactorings recommendation algorithms: are we doing it right? / Novozhilov, Evgenii ; Veselov, Ivan ; Pravilov, Michail; Bryksin, Timofey .

Proceedings of the 3rd International Workshop on Refactoring. 2019.

Research outputpeer-review

TY - GEN

T1 - Evaluation of move method refactorings recommendation algorithms: are we doing it right?

AU - Novozhilov, Evgenii

AU - Veselov, Ivan

AU - Pravilov, Michail

AU - Bryksin, Timofey

PY - 2019/5

Y1 - 2019/5

N2 - Previous studies introduced various techniques for detecting Move Method refactoring opportunities. However, different authors have different evaluations, which leads to the fact that results reported by different papers do not correlate with each other and it is almost impossible to understand which algorithm works better in practice. In this paper, we provide an overview of existing evaluation approaches for Move Method refactoring recommendation algorithms, as well as discuss their advantages and disadvantages. We propose a tool that can be used for generating large synthetic datasets suitable for both algorithms evaluation and building complex machine learning models for Move Method refactoring recommendation

AB - Previous studies introduced various techniques for detecting Move Method refactoring opportunities. However, different authors have different evaluations, which leads to the fact that results reported by different papers do not correlate with each other and it is almost impossible to understand which algorithm works better in practice. In this paper, we provide an overview of existing evaluation approaches for Move Method refactoring recommendation algorithms, as well as discuss their advantages and disadvantages. We propose a tool that can be used for generating large synthetic datasets suitable for both algorithms evaluation and building complex machine learning models for Move Method refactoring recommendation

M3 - Conference contribution

BT - Proceedings of the 3rd International Workshop on Refactoring

ER -

Novozhilov E, Veselov I, Pravilov M, Bryksin T. Evaluation of move method refactorings recommendation algorithms: are we doing it right? In Proceedings of the 3rd International Workshop on Refactoring. 2019