Detecting Bad Smells with Weight Based Distance Metrics Theory

Abstract

Detecting bad smells in program design and implementation is a challenging task. Manual detection is proved to be time-consuming and inaccurate under complex situation. Weight based distance metrics and relevant conceptions are introduced in this paper, and the automatic approach for bad smells detection is proposed based on Jaccard distance. The conception of distance between entities and classes is defined and relevant computing formulas are applied in detecting. New weight based distance metrics theory is proposed to detect feature envy bad smell. This improved approach can express more detailed design quality and invoking relationship than the original distance metrics theory. With these improvements the automation of bad smells detection can be achieved with high accuracy. And then the approach is applied to detect bad smells in JFreeChart open source code. The experimental results show that the weight based distance metrics theory can detect the bad smell more accurately with low time complexity.