On the Role of Data Balancing for Machine Learning-Based Code Smell Detection

Code smells can compromise software quality in the long term by inducing technical debt. For this reason, many approaches aimed at identifying these design flaws have been proposed in the last decade. Most of them are based on heuristics in which a set of metrics (e.g., code metrics, process metrics) is used to detect smelly code components. However, these techniques suffer of subjective interpretation, low agreement between detectors, and threshold dependability. To overcome these limitations, previous work applied Machine Learning techniques that can learn from previous datasets without needing any threshold definition. However, more recent work has shown that Machine Learning is not always suitable for code smell detection due to the highly unbalanced nature of the problem. In this study we investigate several approaches able to mitigate data unbalancing issues to understand their impact on ML-based approaches for code smell detection. Our findings highlight a number of limitations and open issues with respect to the usage of data balancing in ML-based code smell detection.