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Improvement in Software Defect Prediction Outcome Using Principal Component Analysis and Ensemble Machine Learning Algorithms

  • N. DhamayanthiEmail author
  • B. Lavanya
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 26)

Abstract

Improving customer experience is the focus of IT Industry. It is no longer about customer satisfaction, but it is about creating memorable experiences which will help build loyal customers. Hence it is extremely critical to release defect free software. While machine learning techniques were widely used for prediction modelling, creating a reliable predictor which can perform satisfactorily is always a challenge. In this paper, we have proposed a framework using PCA for feature selection and ensemble machine learning algorithms with stratified 10-fold cross validation for building the classification model. The proposed model is tested using 5 projects from NASA Metrics Data program and 4 ensemble machine learning algorithms. Our results show that the prediction accuracy is improved by 0.6% when the reduced dataset is used for classification than using the whole dataset. In comparison with previous research studies, our framework has shown an average of 4.2% increase in performance.

Keywords

Software defect prediction Feature selection Classification Fault proneness Ensemble machine learning algorithms Data mining Fault prediction Dimensionality reduction Machine learning techniques Meta learning NASA Metrics Data Program Principal component analysis Random forest Adaboost Bagging Classification via regression Stratified 10 fold cross validation Software quality Reliable software Prediction modelling 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of MadrasChennaiIndia

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