Detection of Blob and Feature Envy Smells in a Class Diagram using Class's Features

Abstract

Measuring the quality of software design artifacts is difficult due to the limitation of information in the design phase. The class diagram is one of the design artifacts produced during the design phase. The syntactic and semantic information in the class is essential to consider in the measurement process. Smell detection uses class-related information to detect the smell as an indicator of a lack of quality. Several classifiers use all information related to the class to prove how informative it for the smell detection process. The smell types that are a concern in this research are Blob and Feature Envy. The experiment using three classifiers (j48, Multi-Layer Perceptron, and Naïve Bayes) confirms that Blob smell detection utilizes the information successfully. On the other hand, Feature Envy still needs more elaboration. The average true positive rate by the classifiers is about 80.67%.