Class Imbalance Reduction and Centroid based Relevant Project Selection for Cross Project Defect Prediction

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Published: Jun 11, 2023
DOI: https://doi.org/10.17762/ijritcc.v11i6s.6933
Keywords:
Cross Project Defect Prediction, Class imbalance, PF-SMOTE, Software quality assurance

Main Article Content

Kiran Kumar Bejjanki
Associate Professor, Dept. of Information Technology, Kakatiya Institute of Technology & Science Warangal, India
Sai Priyanka Kanchanapally
M.Tech Scholar, Dept. of Information Technology, Kakatiya Institute of Technology & Science Warangal, India
Mahesh Kumar Thota
Assistant Professor, Dept. of Information Technology, Kakatiya Institute of Technology & Science Warangal, India

Abstract

Cross-Project Defect Prediction (CPDP) is the process of predicting defects in a target project using information from other projects. This can assist developers in prioritizing their testing efforts and finding flaws. Transfer Learning (TL) has been frequently used at CPDP to improve prediction performance by reducing the disparity in data distribution between the source and target projects. Software Defect Prediction (SDP) is a common study topic in software engineering that plays a critical role in software quality assurance. To address the cross-project class imbalance problem, Centroid-based PF-SMOTE for Imbalanced data is used. In this paper, we used a Centroid-based PF-SMOTE to balance the datasets and Centroid based relevant data selection for Cross Project Defect Prediction. These methods use the mean of all attributes in a dataset and calculating the difference between mean of all datasets. For experimentation, the open source software defect datasets namely, AEEM, Re-Link, and NASA, are considered.

Article Details

How to Cite
Bejjanki, K. K. ., Kanchanapally, S. P. ., & Thota, M. K. . (2023). Class Imbalance Reduction and Centroid based Relevant Project Selection for Cross Project Defect Prediction. International Journal on Recent and Innovation Trends in Computing and Communication, 11(6s), 293–302. https://doi.org/10.17762/ijritcc.v11i6s.6933
Issue
Vol. 11 No. 6s (2023): Advances in Computational Modeling and Simulation of Computing Systems
Section
Articles

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