Application of support vector machine to predict fault prone classes

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Application of support vector machine to predict fault prone classes

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ACM SIGSOFT Software Engineering Notes archive
Volume 34 , Issue 1 (January 2009) table of contents

SECTION: Article abstracts with full text online table of contents

Pages 1-6

Year of Publication: 2009

ISSN:0163-5948

Authors

Yogesh Singh	Guru Gobind Singh Indraprastha University, Kashmere Gate, Delhi, India
Arvinder Kaur	Guru Gobind Singh Indraprastha University, Kashmere Gate, Delhi, India
Ruchika Malhotra	Guru Gobind Singh Indraprastha University, Kashmere Gate, Delhi, India

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ACM New York, NY, USA

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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.

REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

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