  A code smell is a surface indicator of an inherent problem in the system,
most often due to deviation from standard coding practices on the developers
part during the development phase. Studies observe that code smells made the
code more susceptible to call for modifications and corrections than code that
did not contain code smells. Restructuring the code at the early stage of
development saves the exponentially increasing amount of effort it would
require to address the issues stemming from the presence of these code smells.
Instead of using traditional features to detect code smells, we use user
comments to manually construct features to predict code smells. We use three
Extreme learning machine kernels over 629 packages to identify eight code
smells by leveraging feature engineering aspects and using sampling techniques.
Our findings indicate that the radial basis functional kernel performs best out
of the three kernel methods with a mean accuracy of 98.52.
