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Journal of Computer Science and Technology ›› 2019, Vol. 34 ›› Issue (5): 1020-1038.

Special Issue: Data Management and Data Mining; Software Systems

• Special Section on Software Systems 2019 •

### DP-Share: Privacy-Preserving Software Defect Prediction Model Sharing Through Differential Privacy

Xiang Chen1,2,3, Senior Member, CCF, Dun Zhang1, Zhan-Qi Cui2,4, Member, CCF, Qing Gu2, Senior Member, CCF, Xiao-Lin Ju1,2, Member, CCF

1. 1 School of Information Science and Technology, Nantong University, Nantong 226019, China;
2 State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210023, China;
3 School of Computer Science and Engineering, Nanyang Technological University, Singapore 639798, Singapore;
4 Computer School, Beijing Information Science and Technology University, Beijing 100101, China
• Received:2018-11-29 Revised:2019-04-03 Online:2019-08-31 Published:2019-08-31
• About author:Xiang Chen received his B.S. degree in information management and information system from Xi'an Jiaotong University, Xi'an, in 2002. Then he received his M.S. and Ph.D. degrees in computer software and theory from Nanjing University, Nanjing, in 2008 and 2011 respectively. He is with the School of Information Science and Technology at Nantong University, Nantong, as an associate professor. His research interests are mainly in software maintenance and software testing, such as software defect prediction, security vulnerability prediction, combinatorial testing, regression testing, and software fault localization. He has published over 40 papers in referred journals or conferences, including Information and Software Technology, Journal of Systems and Software, IEEE Transactions on Reliability, Software Quality Journal, Journal of Computer Science and Technology, COMPSAC, APSEC and SAC, etc.
• Supported by:
This work was partially supported by the National Natural Science Foundation of China under Grant Nos. 61702041 and 61872263, the Open Project of State Key Laboratory for Novel Software Technology at Nanjing University under Grant No. KFKT2019B14, the Science and Technology Project of Beijing Municipal Education Commission under Grant No. KM201811232016, the Nantong Application Research Plan under Grant No. JC2018134, and Jiangsu Government Scholarship for Overseas Studies.

In current software defect prediction (SDP) research, most previous empirical studies only use datasets provided by PROMISE repository and this may cause a threat to the external validity of previous empirical results. Instead of SDP dataset sharing, SDP model sharing is a potential solution to alleviate this problem and can encourage researchers in the research community and practitioners in the industrial community to share more models. However, directly sharing models may result in privacy disclosure, such as model inversion attack. To the best of our knowledge, we are the first to apply differential privacy (DP) to privacy-preserving SDP model sharing and then propose a novel method DP-Share, since DP mechanisms can prevent this attack when the privacy budget is carefully selected. In particular, DP-Share first performs data preprocessing for the dataset, such as over-sampling for minority instances (i.e., defective modules) and conducting discretization for continuous features to optimize privacy budget allocation. Then, it uses a novel sampling strategy to create a set of training sets. Finally it constructs decision trees based on these training sets and these decision trees can form a random forest (i.e., model). The last phase of DP-Share uses Laplace and exponential mechanisms to satisfy the requirements of DP. In our empirical studies, we choose nine experimental subjects from real software projects. Then, we use AUC (area under ROC curve) as the performance measure and holdout as our model validation technique. After privacy and utility analysis, we find that DP-Share can achieve better performance than a baseline method DF-Enhance in most cases when using the same privacy budget. Moreover, we also provide guidelines to effectively use our proposed method. Our work attempts to fill the research gap in terms of differential privacy for SDP, which can encourage researchers and practitioners to share more SDP models and then effectively advance the state of the art of SDP.

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