Faseeha Matloob; Shabib Aftab; Ahmed Iqbal

A Framework for Software Defect Prediction Using Feature Selection and Ensemble Learning Techniques

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Author(s)

Faseeha Matloob ^1,* Shabib Aftab ¹ Ahmed Iqbal ¹

1. Department of Computer Science, Virtual University of Pakistan

* Corresponding author.

DOI: https://doi.org/10.5815/ijmecs.2019.12.02

Received: 14 Oct. 2019 / Revised: 22 Oct. 2019 / Accepted: 30 Oct. 2019 / Published: 8 Dec. 2019

Index Terms

Feature Selection, Ensemble Classifier, Hybrid Classifier, Machine Learning Techniques, Software Defect Prediction

Abstract

Testing is one of the crucial activities of software development life cycle which ensures the delivery of high quality product. As software testing consumes significant amount of resources so, if, instead of all software modules, only those are thoroughly tested which are likely to be defective then a high quality software can be delivered at lower cost. Software defect prediction, which has now become an essential part of software testing, can achieve this goal. This research presents a framework for software defect prediction by using feature selection and ensemble learning techniques. The framework consists of four stages: 1) Dataset Selection, 2) Pre Processing, 3) Classification, and 4) Reflection of Results. The framework is implemented on six publically available Cleaned NASA MDP datasets and performance is reflected by using various measures including: F-measure, Accuracy, MCC and ROC. First the performance of all search methods within the framework on each dataset is compared with each other and the method with highest score in each performance measure is identified. Secondly, the results of proposed framework with all search methods are compared with the results of 10 well-known supervised classification techniques. The results reflect that the proposed framework outperformed all of other classification techniques.

Cite This Paper

Faseeha Matloob, Shabib Aftab, Ahmed Iqbal, " A Framework for Software Defect Prediction Using Feature Selection and Ensemble Learning Techniques", International Journal of Modern Education and Computer Science(IJMECS), Vol.11, No.12, pp. 14-20, 2019. DOI:10.5815/ijmecs.2019.12.02

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International Journal of Modern Education and Computer Science (IJMECS)