Code Smell Detection Based on Multi-dimensional Software Data and Complex Networks

Abstract

Code smell is the product of improper design and operation, which may be introduced in many situations. It will cause serious problems for further software development and maintenance. Currently, most code smell detection methods detect through a single type of software data. There are restrictions on detecting code smells with complex definitions and characteristics. In this paper, an approach of applying multi-dimensional software data is proposed. A complex network was built through structural data and historical version data, and code smell instances were determined by searching the network. We designed two smells detection strategies were designed and evaluated them in four open source projects. The results demonstrate that the proposed method has 23% and 15% higher F-measures on Shotgun Surgery and Parallel Inheritance Hierarchy than the existing mainstream detection ways. The code smell detection based on multi-dimensional software data and complex network is effective, and this method of processing multidimensional software data is also applicable for data-driven software research.