Name
Abstract | ||
|---|---|---|
Modularity is an effective evaluation approach for understanding the structural quality of evolutionary software. However, there are many diverse ways to measure it. In this paper, we analyze and compare various modularity metrics that have been studied in different domains to assess their applicability to evolutionary software analysis. Through extensive experiments with artificial DSMs and open-source software, we find that the correlations of those metrics are generally high despite their differences. However, our experiments show that a certain metric can be more sensitive to particular modular factors, hence applying of comprehensive modularity metrics must be taken into consideration. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1587/transinf.2014EDL8047 | IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS |
Keywords | Field | DocType |
modularity, metric, software evolution | Pattern recognition,Software engineering,Computer science,Software,Artificial intelligence,Software evolution,Modularity | Journal |
Volume | Issue | ISSN |
E98D | 2 | 1745-1361 |
Citations | PageRank | References |
0 | 0.34 | 4 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ki-Seong Lee | 1 | 9 | 3.28 |
| Chan-gun Lee | 2 | 99 | 29.51 |