Design Pattern Detection by Using Correlation Feature Selection Technique

Abstract

Design pattern detection is a popular re-engineering job in software development. It provides essential information to the developers to recognize, sustain, and recycle the software. This article proposes a graph matching algorithm using a correlation-based feature selection technique to discover the design pattern (DP) instances from the software or system design. Usually, the feature selection method is used to choose the best instances from the dataset, which mostly matches the target feature. However, this paper has applied a correlation feature selection method to match the system design to the design pattern. We have taken three DPs comparing examples for this intention:•the first example for an entire match•the second example for a partial match•the third example of a not matchIn this experiment, we took the Abstract factory DP as a model graph, and the remaining twenty-two DPs are system or DPs. Software instances groups lots of classes and objects we converted into a graph. We observed that three of the 22 DPs are completely matched to the model DP, one pattern is not matched, and the remaining 18 patterns are only partially matched.