Comparing and experimenting machine learning techniques for code smell detection

Francesca Arcelli Fontana, Mika V. Mäntylä, Marco Zanoni*, Alessandro Marino

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

121 Citations (Scopus)


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 samples. We found that all algorithms achieved high performances in the cross-validation data set, yet the highest performances were obtained by J48 and Random Forest, while the worst performance were achieved by support vector machines. However, the lower prevalence of code smells, i.e., imbalanced data, in the entire data set caused varying performances that need to be addressed in the future studies. We conclude that the application of machine learning to the detection of these code smells can provide high accuracy (>96 %), and only a hundred training examples are needed to reach at least 95 % accuracy.

Original languageEnglish
Pages (from-to)1143-1191
Number of pages49
JournalEmpirical Software Engineering
Issue number3
Publication statusPublished - 1 Jun 2016
MoE publication typeA1 Journal article-refereed


  • Benchmark for code smell detection
  • Code smells detection
  • Machine learning techniques

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