|Table of Contents|

Cross-project Software Defect Prediction Based on Federated Transfer(PDF)

《南京师大学报（自然科学版）》[ISSN:1001-4616/CN:32-1239/N]

Issue:
2024年03期
Page:
122-128
Research Field:
计算机科学与技术
Publishing date:

Info

Title:
Cross-project Software Defect Prediction Based on Federated Transfer
Author(s):
Song Huiling1; 2; Li Yong1; 2; 3; Zhang Wenjing1; 2
(1.College of Computer Science and Technology,Xinjiang Normal University,Urumqi 830054,China)
(2.Software Development Department,Xinjiang Electronic Research Institute,Urumqi 830010,China)
(3.Key Laboratory of Ministry of Industry and Information Technology for Safety-critical Software Development and Verification,Nanjing University of Aeronautics and Astronautics,Nanjing 211106,China)
Keywords:
software defect prediction; federated learning; transfer learning; differential privacy; convolutional neural network
PACS:
TP181; TP311.5
DOI:
10.3969/j.issn.1001-4616.2024.03.015
Abstract:
Cross-project software defect prediction is based on labeled multi-source project data to build a model,which can address the problem of insufficient software historical data and high labeling cost. However,in traditional cross-project defect prediction,the problem of “data-island” caused by source project data holders to protect the business privacy of software data directly affects the model performance of cross-project prediction. Therefore,in this paper,we propose a cross-project software defect prediction method based on federated transfer(FT-CPDP). Firstly,to address the problem of data privacy leaking and feature heterogeneity between projects,this paper presents a model algorithm based on the combination of federal learning and migratory learning to break down the “data barrier” among data holders,and to achieve cross-project defect prediction model in the privacy protection scenario. Secondly,in the federal communication process,the level of privacy protection is increased by adding noise that satisfies the privacy budget. Finally,a convolution neural network model is built to realize software defect prediction. Experiments based on NASA software defect prediction dataset show that compared with traditional cross-project defect prediction methods,FT-CPDP method achieves better comprehensive performance on the premise of software data privacy protection.

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Last Update: 2024-09-15