Paper Info
Title
Multi-Classifier Model For Software Fault Prediction
Abstract
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 National Aeronautics and Space Administration( NASA) Metric Data Program (MDP) projects and Turkish software projects. The experimental result shows that our multi classifier approach which is the combination of Support Vector Machine (SVM), Naive Bayes (NB) and Random forest machine significantly improves the performance of software fault prediction.
Year
Venue
Keywords
2018
INTERNATIONAL ARAB JOURNAL OF INFORMATION TECHNOLOGY
Software metrics, software fault prediction, machine learning
Field
DocType
Volume
Software fault,Pattern recognition,Computer science,Artificial intelligence,Classifier (linguistics),Machine learning
Journal
15
Issue
ISSN
Citations 
5
1683-3198
0
PageRank 
References 
Authors
0.34
7
2
Name
Order
Citations
PageRank
Pradeep Singh1175.62
Verma, Shrish2216.26