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Candidate Project Selection in Cross Project Defect Prediction Using Hybrid Method by Shailza Kanwar; Lalit Kumar Awasthi; Vivek Shrivastava is a Computer Science article available to read on EtoBox.

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Cross Project Defect Prediction (CPDP) is a process that develops a defect prediction model on source projects and then applies the same model to the target project. Day by day, new software projects are being developed, so selecting an appropriate training project from existing projects and from new upcoming projects, to train a predictor model is a challenging task in CPDP. In the present study, we have proposed a hybrid selection method to select a candidate project from existing projects and a probabilistic method to select a candidate project from new projects. The proposed hybrid method is a weighted combination of the Collaborative filtering (CF) method and the Content Based (CB) method. The probabilistic method is based on a Naïve Bayes classifier and is used to predict the relation between the target project and the new target project. In the CF method, a usability score is generated for each project by making use of classification techniques, and the CB method calculates the matching score of candidate projects by using the K-dimensional tree. Finally, both the methods are combined by parallelized hybridization design, and weights for the proposed method are estimated wit

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Author

Shailza Kanwar; Lalit Kumar Awasthi; Vivek Shrivastava

Publisher

Elsevier BV

Published

2023

Language

EN

Field

Computer Science (Physical Sciences)