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Evaluation of Move Method refactorings recommendation algorithms: are we doing it right?

Evgenii Novozhilov; Ivan Veselov; Mikhail Pravilov; Timofey Bryksin

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.

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