  Code Smell, similar to a bad smell, is a surface indication of something
tainted but in terms of software writing practices. This metric is an
indication of a deeper problem lies within the code and is associated with an
issue which is prominent to experienced software developers with acceptable
coding practices. Recent studies have often observed that codes having code
smells are often prone to a higher probability of change in the software
development cycle. In this paper, we developed code smell prediction models
with the help of features extracted from source code to predict eight types of
code smell. Our work also presents the application of data sampling techniques
to handle class imbalance problem and feature selection techniques to find
relevant feature sets. Previous studies had made use of techniques such as
Naive - Bayes and Random forest but had not explored deep learning methods to
predict code smell. A total of 576 distinct Deep Learning models were trained
using the features and datasets mentioned above. The study concluded that the
deep learning models which used data from Synthetic Minority Oversampling
Technique gave better results in terms of accuracy, AUC with the accuracy of
some models improving from 88.47 to 96.84.
