Code smells detection and visualization of software systems

Shin Jie Lee, Xavier Lin, Li Hsiang Lo, Yu Cheng Chen, Jonathan Lee

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Bad smells are symptoms in the source code that indicate possible deeper problems and may serve as drivers for code refactoring. Although efforts have been made on measuring code complexity in object-oriented systems, such as CK metrics, little emphasis has been put on analyzing code smells through a visualization manner. In this paper, we present a system for detecting and visualizing three kinds of code smells of software systems: Long Method, Large Class, and Long Parameter List. Thresholds for identifying the code smells are calculated based on statistics analysis on the source code of 50 open source projects. Code smells are visualized as graphs with colored nodes according to their different severity degrees.

Original languageEnglish
Title of host publicationIntelligent Systems and Applications - Proceedings of the International Computer Symposium, ICS 2014
EditorsWilliam Cheng-Chung Chu, Stephen Jenn-Hwa Yang, Han-Chieh Chao
PublisherIOS Press
Pages1763-1771
Number of pages9
ISBN (Electronic)9781614994831
DOIs
Publication statusPublished - 2015 Jan 1
EventInternational Computer Symposium, ICS 2014 - Taichung, Taiwan
Duration: 2014 Dec 122014 Dec 14

Publication series

NameFrontiers in Artificial Intelligence and Applications
Volume274
ISSN (Print)0922-6389

Other

OtherInternational Computer Symposium, ICS 2014
CountryTaiwan
CityTaichung
Period14-12-1214-12-14

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence

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