Cross-project defect prediction using ant colony optimization

  • Authors

    • Kiran Kumar B Kakatiya Institute of Technology & Science, Warangal
    • Dr. Jayadev Gyani Majmaah University
    • Dr. Narsimha G. JNTUH
    2019-08-03
    https://doi.org/10.14419/ijet.v7i4.22264
  • Ant Colony Optimization, Classification, Cross-Project Defect Prediction, Data Mining, Meta-heuristic.

  • Abstract

    Software defect prediction techniques applied on single project are showcasing good results because of availability voluminous data to train the model. But newly developed software projects may not have sufficient amount data to train the model. In cross-project defect prediction model (CPDP), training model is constructed by using defect dataset of one project (which contains sufficient amount of data) and tested on another project (which contains less amount of data). In this paper, we selected similar features from eight open source defect datasets from PROMISE repository and applied meta-heuristic Ant Colony Optimization (ACO) algorithm for Cross-Project defect Prediction.

     

     

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  • How to Cite

    Kumar B, K., Jayadev Gyani, D., & Narsimha G., D. (2019). Cross-project defect prediction using ant colony optimization. International Journal of Engineering & Technology, 7(4), 7068-7071. https://doi.org/10.14419/ijet.v7i4.22264

    Received date: 2018-11-29

    Accepted date: 2018-12-05

    Published date: 2019-08-03