Name
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. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.3233/978-1-61499-484-8-1763 | Frontiers in Artificial Intelligence and Applications |
Keywords | Field | DocType |
Code smell,code visualization,code analysis | Software engineering,Visualization,Computer science,Software system,Software visualization,Code smell | Conference |
Volume | ISSN | Citations |
274 | 0922-6389 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shin-jie Lee | 1 | 81 | 10.56 |
| Xavier Lin | 2 | 0 | 0.34 |
| Li Hsiang Lo | 3 | 0 | 0.34 |
| Yu-Cheng Chen | 4 | 19 | 2.58 |
| jonathan lee | 5 | 5 | 2.26 |