  The sound identification of refactoring opportunities is still an open
problem in software engineering. Recent studies have shown the effectiveness of
machine learning models in recommending methods that should undergo different
refactoring operations. In this work, we experiment with such approaches to
identify methods that should undergo an Extract Method refactoring, in the
context of ING, a large financial organization. More specifically, we (i)
compare the code metrics distributions, which are used as features by the
models, between open-source and ING systems, (ii) measure the accuracy of
different machine learning models in recommending Extract Method refactorings,
(iii) compare the recommendations given by the models with the opinions of ING
experts. Our results show that the feature distributions of ING systems and
open-source systems are somewhat different, that machine learning models can
recommend Extract Method refactorings with high accuracy, and that experts tend
to agree with most of the recommendations of the model.
