A FRAMEWORK FOR SOFTWARE DEFECT PREDICTION USING ENSEMBLE LEARNING
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Author:
UMAIR ALI
Citable URI :
https://vspace.vu.edu.pk/detail.aspx?id=333
Publisher :
Virtual University
Date Issued:
7/4/2020 12:00:00 AM
Abstract
The development of high-quality software at lower cost has always been the main concern of the
developers as well as of the users. Eliminating the defects in software at the initial development
stage can increase quality and reduce the overall cost. Testing only those modules which are likely
to be defective are helpful for development team to manage and use resources effectively. Many
machine learning-based frameworks have been proposed for the prediction of software defects in
initial development stage however accuracy evaluation of proposed techniques on benchmark
datasets was lacked. In this research, we proposed a framework for the prediction of software
defects using ensemble learning and feature selection techniques by using WEKA. The accuracy
of the proposed model has been evaluated by using publicly available cleaned NASA datasets.
Moreover, the results have been compared with the widely used advanced classification
techniques. The Proposed framework consists of five stages. First stage is dealing with the
extraction of relevant dataset. Second stage is dealing with variants of base classifiers and
selection. The base classifiers include: “Decision Tree (DT), K-nearest neighbor (kNN), Naive
Bayes (NB), Random forest (RF) and Support Vector Machine (SVM)”. Pre-processing and
feature selection have been done in third stage. In fourth stage, we used stacking technique to
create an ensemble of the classifier-variants, which have performed well in third stage. Fifth stage
deals with the results and performance evaluation by using different measures including:
“Precision, Recall, F-measure, Accuracy, MCC and ROC”.
URI :
https://vspace.vu.edu.pk/details.aspx?id=333
Citation:
Ali,Umair(2019).A FRAMEWORK FOR SOFTWARE DEFECT PREDICTION USING ENSEMBLE LEARNING. Virtual University of Pakistan(Lahore, Pakistan)
Version :
Final Version
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