Empirical Analysis on Effectiveness of NLP Methods for Predicting Code Smell
This work addresses the need for early detection of code smells to reduce maintenance effort, but it is incremental as it applies existing machine learning methods to a new feature set.
The paper tackled the problem of predicting code smells in software development by using user comments to manually construct features, achieving a mean accuracy of 98.52% with a radial basis functional kernel on 629 packages.
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.