Cross-project defect prediction using ant colony optimization

 
 
 
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  • 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.

     

     


  • Keywords


    Ant Colony Optimization; Classification; Cross-Project Defect Prediction; Data Mining; Meta-heuristic.

  • References


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Article ID: 22264
 
DOI: 10.14419/ijet.v7i4.22264




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