Does it smell? A homogeneous stacking approach for code smell prediction

Context:

Code smells, defined as detrimental patterns and design choices in software development, significantly impact various aspects of software quality, such as maintainability, reusability, and stability. These harmful effects can disrupt the software development cycle and result in a waste of development and managerial resources.

Objective:

Although code smell detection has attracted considerable attention in recent years, the existing literature still shows certain limitations whereby most of the studies have been conducted on small data sets, a small number of code smells at once and evaluated using few performance metrics.

Methods:

In this work, we propose a Homogeneous Stacking Classifier to predict the presence of nine different code smells. We resort to feature selection to keep the attributes relevant to each code smell.

Results:

We use a large data set of 19,000 instances and we evaluate the performance of our proposed model using eight different metrics comparing it to state-of-the-art machine learning techniques that have proven to perform well in current research.

Conclusion:

The proposed approach statistically significantly outperforms the other models across most cases therefore, affirming its efficacy in code smell detection.