Name
Abstract | ||
|---|---|---|
Empirical validation of software metrics to predict quality using machine learning methods is important to ensure their practical relevance in the software organizations. It would also be interesting to know the relationship between object-oriented metrics and fault proneness. In this paper, we build a Support Vector Machine (SVM) model to find the relation-ship between object-oriented metrics given by Chidamber and Kemerer and fault proneness. The proposed model is empirically evaluated using open source software. The performance of the SVM method was evaluated by Receiver Operating Characteristic (ROC) analysis. Based on these results, it is reasonable to claim that such models could help for planning and performing testing by focusing resources on fault- prone parts of the design and code. Thus, the study shows that SVM method may also be used in constructing software quality models. However, similar types of studies are required to be carried out in order to establish the acceptability of the model. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1145/1457516.1457529 | ACM SIGSOFT Software Engineering Notes |
Keywords | Field | DocType |
open source software,support vector machine,object-oriented metrics,software metrics,software organization,fault prone class,fault proneness,receiver operating characteristic,software quality model,svm method,machine learning,software metric,receiver operator characteristic,roc analysis,software quality | Data mining,Receiver operating characteristic,Fault prone,Computer science,Support vector machine,Software,Software reliability testing,Artificial intelligence,Software metric,Software quality,Machine learning,Software sizing | Journal |
Volume | Issue | Citations |
34 | 1 | 3 |
PageRank | References | Authors |
0.42 | 29 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yogesh Singh | 1 | 267 | 13.87 |
| Arvinder Kaur | 2 | 370 | 26.99 |
| Ruchika Malhotra | 3 | 533 | 35.12 |