Multi-classifier model for software fault prediction
Joint Authors
Source
The International Arab Journal of Information Technology
Issue
Vol. 15, Issue 5 (30 Sep. 2018)8 p.
Publisher
Publication Date
2018-09-30
Country of Publication
Jordan
No. of Pages
8
Main Subjects
Information Technology and Computer Science
Abstract EN
Prediction of fault prone module prior to testing is an emerging activity for software organizations to allocate targeted resource for development of reliable software.
These software fault prediction depend on the quality of fault and related code extracted from previous versions of software.
This paper, presents a novel framework by combining multiple expert machine learning systems.
The proposed multi-classifier model takes the benefits of best classifiers in deciding the faulty modules of software system with consensus prior to testing.
An experimental comparison is performed with various outperformer classifiers in the area of fault prediction.
We evaluate our approach on 16 public dataset from promise repository which consists of NASA MDP projects and Turkish software projects.
The experimental result shows that our multi classifier approach which is the combination of SVM, Naive Bayes and Random forest machine significantly improves the performance of software fault prediction
American Psychological Association (APA)
Singh, Pradeep& Verma, Shrish. 2018. Multi-classifier model for software fault prediction. The International Arab Journal of Information Technology،Vol. 15, no. 5.
https://search.emarefa.net/detail/BIM-839113
Modern Language Association (MLA)
Singh, Pradeep& Verma, Shrish. Multi-classifier model for software fault prediction. The International Arab Journal of Information Technology Vol. 15, no. 5 (Sep. 2018).
https://search.emarefa.net/detail/BIM-839113
American Medical Association (AMA)
Singh, Pradeep& Verma, Shrish. Multi-classifier model for software fault prediction. The International Arab Journal of Information Technology. 2018. Vol. 15, no. 5.
https://search.emarefa.net/detail/BIM-839113
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-839113