Comparing and experimenting machine learning techniques for code smell detection

@article{Fontana2015ComparingAE,
  title={Comparing and experimenting machine learning techniques for code smell detection},
  author={Francesca Arcelli Fontana and Mika Viking M{\"a}ntyl{\"a} and Marco Zanoni and Alessandro Marino},
  journal={Empirical Software Engineering},
  year={2015},
  volume={21},
  pages={1143-1191}
}
Several code smell detection tools have been developed providing different results, because smells can be subjectively interpreted, and hence detected, in different ways. In this paper, we perform the largest experiment of applying machine learning algorithms to code smells to the best of our knowledge. We experiment 16 different machine-learning algorithms on four code smells (Data Class, Large Class, Feature Envy, Long Method) and 74 software systems, with 1986 manually validated code smell… CONTINUE READING
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