Name
Title | ||
|---|---|---|
Empirical validation of object-oriented metrics for predicting fault proneness at different severity levels using support vector machines |
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 at different severity levels. In this paper, we build a Support vector machine (SVM) model to find the
relationship between object-oriented metrics given by Chidamber and Kemerer and fault proneness, at different severity levels.
The proposed models at different severity levels are empirically evaluated using public domain NASA data set. 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. The performance of the model predicted using high severity faults is low as compared to performance of the
model predicted with respect to medium and low severity faults. 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 |
|---|---|---|
2010 | 10.1007/s13198-011-0048-7 | Int. J. Systems Assurance Engineering and Management |
Keywords | Field | DocType |
metricsobject-orientedsoftware quality � empirical validationfault predictionsupport vector machinereceiver operating characteristics analysis abbreviations coupling coupling is a measure of the degree of interdependence between classes,public domain,receiver operator characteristic,roc analysis,software quality,software metric,machine learning,support vector machine | Data mining,Receiver operating characteristic,Public domain,Object-oriented programming,Computer science,Support vector machine,Software,Artificial intelligence,Software metric,Software quality,Machine learning,Reliability engineering | Journal |
Volume | Issue | ISSN |
1 | 3 | 0976-4348 |
Citations | PageRank | References |
9 | 0.52 | 27 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ruchika Malhotra | 1 | 533 | 35.12 |
| Arvinder Kaur | 2 | 370 | 26.99 |
| Yogesh Singh | 3 | 9 | 0.52 |