Standard

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

IWOR '19: Proceedings of the 3rd International Workshop on Refactoring. 2019. p. 23-26 8844425.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review

Harvard

Novozhilov, E, Veselov, I, Pravilov, M & Bryksin, T 2019, Evaluation of move method refactorings recommendation algorithms: are we doing it right? in IWOR '19: Proceedings of the 3rd International Workshop on Refactoring., 8844425, pp. 23-26, 3rd International Workshop on Refactoring , Montreal, Quebec, Canada, 28/05/19. https://doi.org/10.1109/IWoR.2019.00012

APA

Novozhilov, E., Veselov, I., Pravilov, M., & Bryksin, T. (2019). Evaluation of move method refactorings recommendation algorithms: are we doing it right? In IWOR '19: Proceedings of the 3rd International Workshop on Refactoring (pp. 23-26). [8844425] https://doi.org/10.1109/IWoR.2019.00012

Vancouver

Novozhilov E, Veselov I, Pravilov M, Bryksin T. Evaluation of move method refactorings recommendation algorithms: are we doing it right? In IWOR '19: Proceedings of the 3rd International Workshop on Refactoring. 2019. p. 23-26. 8844425 https://doi.org/10.1109/IWoR.2019.00012

Author

Novozhilov, Evgenii ; Veselov, Ivan ; Pravilov, Michail ; Bryksin, Timofey . / Evaluation of move method refactorings recommendation algorithms: are we doing it right?. IWOR '19: Proceedings of the 3rd International Workshop on Refactoring. 2019. pp. 23-26

BibTeX

@inproceedings{6b928748c469428a861161802e828b11,
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",
keywords = "Algorithms evaluation, Automatic refactoring recommendation, Code smells, Dataset generation, Feature envy, Move method refactoring",
author = "Evgenii Novozhilov and Ivan Veselov and Michail Pravilov and Timofey Bryksin",
year = "2019",
month = may,
doi = "10.1109/IWoR.2019.00012",
language = "English",
pages = "23--26",
booktitle = "IWOR '19: Proceedings of the 3rd International Workshop on Refactoring",
note = "3rd International Workshop on Refactoring , IWOR '19 ; Conference date: 28-05-2019 Through 28-05-2019",

}

RIS

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

KW - Algorithms evaluation

KW - Automatic refactoring recommendation

KW - Code smells

KW - Dataset generation

KW - Feature envy

KW - Move method refactoring

UR - http://www.scopus.com/inward/record.url?scp=85073415385&partnerID=8YFLogxK

U2 - 10.1109/IWoR.2019.00012

DO - 10.1109/IWoR.2019.00012

M3 - Conference contribution

SP - 23

EP - 26

BT - IWOR '19: Proceedings of the 3rd International Workshop on Refactoring

T2 - 3rd International Workshop on Refactoring

Y2 - 28 May 2019 through 28 May 2019

ER -

ID: 43773876