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How far does the predictive decision impact the software project? The cost, service time, and failure analysis from a cross-project defect prediction model

Publication Type : Conference Proceedings

Source : Journal of Systems and Software Volume 195, January 2023

Url : https://www.sciencedirect.com/science/article/abs/pii/S0164121222001984

Campus : Coimbatore

School : School of Computing

Year : 2023

Abstract : Cross-project defect prediction (CPDP) models are being developed to optimise the testing resources. Objectives: Proposing an ensemble classification framework for CPDP as many existing models are lacking with better performances and analysing the main objectives of CPDP from the outcomes of the proposed classification framework. Method: For the classification task, we propose a bootstrap aggregation based hybrid-inducer ensemble learning (HIEL) technique that uses probabilistic weighted majority voting (PWMV) strategy. To know the impact of HIEL on the software project, we propose three project-specific performance measures such as percent of perfect cleans (PPC), percent of non-perfect cleans (PNPC), and false omission rate (FOR) from the predictions to calculate the amount of saved cost, remaining service time, and percent of the failures in the target project.

Cite this Research Publication : Umamaheswara Sharma B, Ravichandra Sadam "How far does the predictive decision impact the software project? The cost, service time, and failure analysis from a cross-project defect prediction model", Journal of Systems and Software
Volume 195, January 2023

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