  Code smells are characteristics of the software that indicates a code or
design problem which can make software hard to understand, evolve, and
maintain. The code smell detection tools proposed in the literature produce
different results, as smells are informally defined or are subjective in
nature. To address the issue of tool subjectivity, machine learning techniques
have been proposed which can learn and distinguish the characteristics of
smelly and non-smelly source code elements (classes or methods). However, the
existing machine learning techniques can only detect a single type of smell in
the code element which does not correspond to a real-world scenario. In this
paper, we have used multilabel classification methods to detect whether the
given code element is affected by multiple smells or not. We have considered
two code smell datasets for this work and converted them into a multilabel
dataset. In our experimentation, Two multilabel methods performed on the
converted dataset which demonstrates good performances in the 10-fold
cross-validation, using ten repetitions.
