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17 August 2021
A Novel Cross-Project Software Defect Prediction Algorithm Based on Transfer Learning
Song Huang(
),
Changyou Zheng(
),
),
Changyou Zheng(
),
Cite this article:
Tang S, Huang S, Zheng C, et al.
A Novel Cross-Project Software Defect Prediction Algorithm Based on Transfer Learning.
Tsinghua Science and Technology,
2022, 27(1): 41-57.
https://doi.org/10.26599/TST.2020.9010040
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Software Defect Prediction (SDP) technology is an effective tool for improving software system quality that has attracted much attention in recent years. However, the prediction of cross-project data remains a challenge for the traditional SDP method due to the different distributions of the training and testing datasets. Another major difficulty is the class imbalance issue that must be addressed in Cross-Project Defect Prediction (CPDP). In this work, we propose a transfer-leaning algorithm (TSboostDF) that considers both knowledge transfer and class imbalance for CPDP. The experimental results demonstrate that the performance achieved by TSboostDF is better than those of existing CPDP methods.
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A Novel Cross-Project Software Defect Prediction Algorithm Based on Transfer Learning
Song Huang(
), Changyou Zheng(
),
), Changyou Zheng(
),
Abstract
Software Defect Prediction (SDP) technology is an effective tool for improving software system quality that has attracted much attention in recent years. However, the prediction of cross-project data remains a challenge for the traditional SDP method due to the different distributions of the training and testing datasets. Another major difficulty is the class imbalance issue that must be addressed in Cross-Project Defect Prediction (CPDP). In this work, we propose a transfer-leaning algorithm (TSboostDF) that considers both knowledge transfer and class imbalance for CPDP. The experimental results demonstrate that the performance achieved by TSboostDF is better than those of existing CPDP methods.
Keywords:
transfer learning, Software Defect Prediction (SDP), imbalance class, cross-project
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Acknowledgements
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Publication history
Received: 28 August 2020
Accepted: 18 September 2020
Published:
17 August 2021
Issue date: February 2022
Copyright
© The author(s) 2022
Acknowledgements
This work was partially supported by the Army Weapons and Equipment Internal Research (No. LJ20191C080690).
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The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
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