Multi-Classifier
Model for Software Fault Prediction
Pradeep
Singh1 and Shrish Verma2
1Department
of Computer Science and Engineering, National Institute of Technology, Raipur
2Department
of Electronics and Telecommunication Engineering, National Institute of Technology, Raipur
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.
Keywords: Software metrics, software fault
prediction, machine learning.
Received February 7, 2015; accepted September 7, 2015