Ahmed Iqbal; Shabib Aftab

Prediction of Defect Prone Software Modules using MLP based Ensemble Techniques

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Author(s)

Ahmed Iqbal ^1,* Shabib Aftab ¹

1. Department of Computer Science, Virtual University of Pakistan, Lahore, Pakistan

* Corresponding author.

DOI: https://doi.org/10.5815/ijitcs.2020.03.04

Received: 9 Nov. 2019 / Revised: 20 Nov. 2019 / Accepted: 27 Nov. 2019 / Published: 8 Jun. 2020

Index Terms

Software Defect Prediction, MLP, Ensemble Classification techniques, Software Metrics

Abstract

Prediction of defect prone software modules is now considered as an important activity of software quality assurance. This approach uses the software metrics to predict whether the developed module is defective or not. This research presents MLP based ensemble classification framework to predict the defect prone software modules. The framework predicts the defective modules by using three dimensions: 1) Tuned MLP, 2) Tuned MLP with Bagging 3) Tuned MLP with Boosting. In first dimension only the MLP is used for the classification after optimization. In second dimension, the optimized MLP is integrated with bagging technique. In third dimension, the optimized MLP is integrated with boosting technique. Four publically available cleaned NASA MDP datasets are used for the implementation of proposed framework and the performance is evaluated by using F-measure, Accuracy, Roc Area and MCC. The performance of the proposed framework is compared with ten widely used supervised classification techniques by using Scott-Knott ESD test and the results reflects the high performance of the proposed framework.

Cite This Paper

Ahmed Iqbal, Shabib Aftab, "Prediction of Defect Prone Software Modules using MLP based Ensemble Techniques", International Journal of Information Technology and Computer Science(IJITCS), Vol.12, No.3, pp.26-31, 2020. DOI:10.5815/ijitcs.2020.03.04

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International Journal of Information Technology and Computer Science (IJITCS)