sis 20(25): e5

Research Article

Prediction of Cross Project Defects using Ensemble based Multinomial Classifier

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  • @ARTICLE{10.4108/eai.13-7-2018.159974,
        author={Lipika  Goel and Mayank  Sharma and Sunil  Kumar  Khatri and D. Damodaran},
        title={Prediction of Cross Project Defects using Ensemble based Multinomial Classifier},
        journal={EAI Endorsed Transactions on Scalable Information Systems},
        volume={7},
        number={25},
        publisher={EAI},
        journal_a={SIS},
        year={2019},
        month={9},
        keywords={Defect Prediction, CPDP (Cross Project defect prediction), WPDP (Within Project defect prediction), Ensemble learning, Multinomial classification, Homogeneous metrics},
        doi={10.4108/eai.13-7-2018.159974}
    }
    
  • Lipika Goel
    Mayank Sharma
    Sunil Kumar Khatri
    D. Damodaran
    Year: 2019
    Prediction of Cross Project Defects using Ensemble based Multinomial Classifier
    SIS
    EAI
    DOI: 10.4108/eai.13-7-2018.159974
Lipika Goel1,2,*, Mayank Sharma3, Sunil Kumar Khatri3, D. Damodaran4
  • 1: Research Scholar, Amity University Noida, India
  • 2: Assistant Professor, AKG Engineering College GZB, India
  • 3: Amity Institute of Information Technology, Noida, India
  • 4: Centre for Reliability, Chennai, India
*Contact email: lipika.bose@gmail.com

Abstract

BACKGROUND: The availability of defect related data of different projects leads to cross project defect prediction an open issue. Many studies have focused on analyzing and improving the performance of Cross project defect prediction.

OBJECTIVE: The multinomial classification has not been much explored. This paper instanced on multiclass/multinomial classification of defect prediction of cross projects.

METHOD: The ensemble based statistical models – Gradient Boosting and Random Forest are used for classification. An empirical study is carried out to determine the performance of multinomial classification for cross project defect prediction. Depending on the number of defects, class level information is classified into one of three defined multiclass class 0, class 1, and class 2.

RESULTS & CONCLUSION: Major outcome of the paper concludes that multinomial/multiclass classification is applicable on cross project data and has comparable results to within project defect data.