Based on Multi-dimensional Software Data for Code Smell Detection

Code smell is the product of improper design and operation, which may be introduced in many situations. It will cause problems for further development and maintenance. Currently, most code smells detection tools detect through a single type of data, and are primarily based on software structured data. There are restrictions on detecting code smells with complex definitions and characteristics. In this paper, we propose a method of applying complex network technology combined with multi-dimensional software data, which can comprehensively use more and richer software data. We designed two smells detection strategies and evaluated them in four open source projects. Judging from the results, we demonstrate that our approach has 23% and 15% higher F-measures on Shotgun Surgery and Parallel Inheritance Hierarchy than the existing most advanced detection tools. The detection method based on multi-dimensional software data and complex network is effective. This software network also provides guidance for data-driven software research.