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  2022 (1)
Lie to Me: Abusing the Mobile Content Sharing Service for Fun and Profit. Xu, G.; Li, S.; Zhou, H.; Liu, S.; Tang, Y.; Li, L.; Luo, X.; Xiao, X.; Xu, G.; and Wang, H. In Laforest, F.; Troncy, R.; Simperl, E.; Agarwal, D.; Gionis, A.; Herman, I.; and Médini, L., editor(s), WWW '22: The ACM Web Conference 2022, Virtual Event, Lyon, France, April 25 - 29, 2022, pages 3327–3335, 2022. ACM
Lie to Me: Abusing the Mobile Content Sharing Service for Fun and Profit [link]Paper   doi   link   bibtex  
  2021 (8)
AComNN: Attention enhanced Compound Neural Network for financial time-series forecasting with cross-regional features. Yang, Z.; Keung, J.; Kabir, M. A.; Yu, X.; Tang, Y.; Zhang, M.; and Feng, S. Appl. Soft Comput., 111: 107649. 2021.
AComNN: Attention enhanced Compound Neural Network for financial time-series forecasting with cross-regional features [link]Paper   doi   link   bibtex  
Feature selection and embedding based cross project framework for identifying crashing fault residence. Xu, Z.; Zhang, T.; Keung, J.; Yan, M.; Luo, X.; Zhang, X.; Xu, L.; and Tang, Y. Inf. Softw. Technol., 131: 106452. 2021.
Feature selection and embedding based cross project framework for identifying crashing fault residence [link]Paper   doi   link   bibtex  
Simplified Deep Forest Model Based Just-in-Time Defect Prediction for Android Mobile Apps. Zhao, K.; Xu, Z.; Zhang, T.; Tang, Y.; and Yan, M. IEEE Trans. Reliab., 70(2): 848–859. 2021.
Simplified Deep Forest Model Based Just-in-Time Defect Prediction for Android Mobile Apps [link]Paper   doi   link   bibtex  
Object-Level Remote Sensing Image Augmentation Using U-Net-Based Generative Adversarial Networks. Huang, J.; Liu, S.; Tang, Y.; and Zhang, X. Wirel. Commun. Mob. Comput., 2021: 1230279:1–1230279:12. 2021.
Object-Level Remote Sensing Image Augmentation Using U-Net-Based Generative Adversarial Networks [link]Paper   doi   link   bibtex  
BRNN-GAN: Generative Adversarial Networks with Bi-directional Recurrent Neural Networks for Multivariate Time Series Imputation. Wu, Z.; Ma, C.; Shi, X.; Wu, L.; Zhang, D.; Tang, Y.; and Stojmenovic, M. In 27th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2021, Beijing, China, December 14-16, 2021, pages 217–224, 2021. IEEE
BRNN-GAN: Generative Adversarial Networks with Bi-directional Recurrent Neural Networks for Multivariate Time Series Imputation [link]Paper   doi   link   bibtex  
Just-in-time defect prediction for Android apps via imbalanced deep learning model. Zhao, K.; Xu, Z.; Yan, M.; Tang, Y.; Fan, M.; and Catolino, G. In Hung, C.; Hong, J.; Bechini, A.; and Song, E., editor(s), SAC '21: The 36th ACM/SIGAPP Symposium on Applied Computing, Virtual Event, Republic of Korea, March 22-26, 2021, pages 1447–1454, 2021. ACM
Just-in-time defect prediction for Android apps via imbalanced deep learning model [link]Paper   doi   link   bibtex  
A Systematical Study on Application Performance Management Libraries for Apps. Tang, Y.; Wang, H.; Zhan, X.; Luo, X.; Zhou, Y.; Zhou, H.; Yan, Q.; Sui, Y.; and Keung, J. CoRR, abs/2103.11286. 2021.
A Systematical Study on Application Performance Management Libraries for Apps [link]Paper   link   bibtex  
Towards Black-box Attacks on Deep Learning Apps. Cao, H.; Li, S.; Zhou, Y.; Fan, M.; Zhao, X.; and Tang, Y. CoRR, abs/2107.12732. 2021.
Towards Black-box Attacks on Deep Learning Apps [link]Paper   link   bibtex  
  2020 (5)
Demystifying Diehard Android Apps. Zhou, H.; Wang, H.; Zhou, Y.; Luo, X.; Tang, Y.; Xue, L.; and Wang, T. In 35th IEEE/ACM International Conference on Automated Software Engineering, ASE 2020, Melbourne, Australia, September 21-25, 2020, pages 187–198, 2020. IEEE
Demystifying Diehard Android Apps [link]Paper   doi   link   bibtex  
Simplified Deep Forest Model based Just-In-Time Defect Prediction for Android Mobile Apps. Zhao, K.; Xu, Z.; Zhang, T.; and Tang, Y. In 20th IEEE International Conference on Software Quality, Reliability and Security, QRS 2020, Macau, China, December 11-14, 2020, pages 222, 2020. IEEE
Simplified Deep Forest Model based Just-In-Time Defect Prediction for Android Mobile Apps [link]Paper   doi   link   bibtex  
All your app links are belong to us: understanding the threats of instant apps based attacks. Tang, Y.; Sui, Y.; Wang, H.; Luo, X.; Zhou, H.; and Xu, Z. In Devanbu, P.; Cohen, M. B.; and Zimmermann, T., editor(s), ESEC/FSE '20: 28th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering, Virtual Event, USA, November 8-13, 2020, pages 914–926, 2020. ACM
All your app links are belong to us: understanding the threats of instant apps based attacks [link]Paper   doi   link   bibtex  
Resource Race Attacks on Android. Cai, Y.; Tang, Y.; Li, H.; Yu, L.; Zhou, H.; Luo, X.; He, L.; and Su, P. In Kontogiannis, K.; Khomh, F.; Chatzigeorgiou, A.; Fokaefs, M.; and Zhou, M., editor(s), 27th IEEE International Conference on Software Analysis, Evolution and Reengineering, SANER 2020, London, ON, Canada, February 18-21, 2020, pages 47–58, 2020. IEEE
Resource Race Attacks on Android [link]Paper   doi   link   bibtex  
Feature Location Benchmark for Decomposing and Reusing Android Apps. Tang, Y.; Zhou, H.; Xu, Z.; Luo, X.; Cai, Y.; and Zhang, T. CoRR, abs/2005.04008. 2020.
Feature Location Benchmark for Decomposing and Reusing Android Apps [link]Paper   link   bibtex  
  2019 (8)
Software defect prediction based on kernel PCA and weighted extreme learning machine. Xu, Z.; Liu, J.; Luo, X.; Yang, Z.; Zhang, Y.; Yuan, P.; Tang, Y.; and Zhang, T. Inf. Softw. Technol., 106: 182–200. 2019.
Software defect prediction based on kernel PCA and weighted extreme learning machine [link]Paper   doi   link   bibtex  
Cross Project Defect Prediction via Balanced Distribution Adaptation Based Transfer Learning. Xu, Z.; Pang, S.; Zhang, T.; Luo, X.; Liu, J.; Tang, Y.; Yu, X.; and Xue, L. J. Comput. Sci. Technol., 34(5): 1039–1062. 2019.
Cross Project Defect Prediction via Balanced Distribution Adaptation Based Transfer Learning [link]Paper   doi   link   bibtex  
TSTSS: A two-stage training subset selection framework for cross version defect prediction. Xu, Z.; Li, S.; Luo, X.; Liu, J.; Zhang, T.; Tang, Y.; Xu, J.; Yuan, P.; and Keung, J. J. Syst. Softw., 154: 59–78. 2019.
TSTSS: A two-stage training subset selection framework for cross version defect prediction [link]Paper   doi   link   bibtex  
LDFR: Learning deep feature representation for software defect prediction. Xu, Z.; Li, S.; Xu, J.; Liu, J.; Luo, X.; Zhang, Y.; Zhang, T.; Keung, J.; and Tang, Y. J. Syst. Softw., 158. 2019.
LDFR: Learning deep feature representation for software defect prediction [link]Paper   doi   link   bibtex  
Identifying Crashing Fault Residence Based on Cross Project Model. Xu, Z.; Zhang, T.; Zhang, Y.; Tang, Y.; Liu, J.; Luo, X.; Keung, J.; and Cui, X. In Wolter, K.; Schieferdecker, I.; Gallina, B.; Cukier, M.; Natella, R.; Ivaki, N. R.; and Laranjeiro, N., editor(s), 30th IEEE International Symposium on Software Reliability Engineering, ISSRE 2019, Berlin, Germany, October 28-31, 2019, pages 183–194, 2019. IEEE
Identifying Crashing Fault Residence Based on Cross Project Model [link]Paper   doi   link   bibtex  
Demystifying Application Performance Management Libraries for Android. Tang, Y.; Zhan, X.; Zhou, H.; Luo, X.; Xu, Z.; Zhou, Y.; and Yan, Q. In 34th IEEE/ACM International Conference on Automated Software Engineering, ASE 2019, San Diego, CA, USA, November 11-15, 2019, pages 682–685, 2019. IEEE
Demystifying Application Performance Management Libraries for Android [link]Paper   doi   link   bibtex  
MVSE: Effort-Aware Heterogeneous Defect Prediction via Multiple-View Spectral Embedding. Xu, Z.; Ye, S.; Zhang, T.; Xia, Z.; Pang, S.; Wang, Y.; and Tang, Y. In 19th IEEE International Conference on Software Quality, Reliability and Security, QRS 2019, Sofia, Bulgaria, July 22-26, 2019, pages 10–17, 2019. IEEE
MVSE: Effort-Aware Heterogeneous Defect Prediction via Multiple-View Spectral Embedding [link]Paper   doi   link   bibtex  
A Comparative Study of Android Repackaged Apps Detection Techniques. Zhan, X.; Zhang, T.; and Tang, Y. In Wang, X.; Lo, D.; and Shihab, E., editor(s), 26th IEEE International Conference on Software Analysis, Evolution and Reengineering, SANER 2019, Hangzhou, China, February 24-27, 2019, pages 321–331, 2019. IEEE
A Comparative Study of Android Repackaged Apps Detection Techniques [link]Paper   doi   link   bibtex  
  2018 (3)
HDA: Cross-Project Defect Prediction via Heterogeneous Domain Adaptation With Dictionary Learning. Xu, Z.; Yuan, P.; Zhang, T.; Tang, Y.; Li, S.; and Xia, Z. IEEE Access, 6: 57597–57613. 2018.
HDA: Cross-Project Defect Prediction via Heterogeneous Domain Adaptation With Dictionary Learning [link]Paper   doi   link   bibtex  
A Smart Context-Aware Program Assistant Based on Dynamic Programming Event Modeling. Zhao, X.; Li, H.; Tang, Y.; Gao, D.; Bao, L.; and Lee, C. In Ghosh, S.; Natella, R.; Cukic, B.; Poston, R. S.; and Laranjeiro, N., editor(s), 2018 IEEE International Symposium on Software Reliability Engineering Workshops, ISSRE Workshops, Memphis, TN, USA, October 15-18, 2018, pages 24–29, 2018. IEEE Computer Society
A Smart Context-Aware Program Assistant Based on Dynamic Programming Event Modeling [link]Paper   doi   link   bibtex  
Cross version defect prediction with representative data via sparse subset selection. Xu, Z.; Li, S.; Tang, Y.; Luo, X.; Zhang, T.; Liu, J.; and Xu, J. In Khomh, F.; Roy, C. K.; and Siegmund, J., editor(s), Proceedings of the 26th Conference on Program Comprehension, ICPC 2018, Gothenburg, Sweden, May 27-28, 2018, pages 132–143, 2018. ACM
Cross version defect prediction with representative data via sparse subset selection [link]Paper   doi   link   bibtex  
  2017 (2)
Constructing feature model by identifying variability-aware modules. Tang, Y.; and Leung, H. In Scanniello, G.; Lo, D.; and Serebrenik, A., editor(s), Proceedings of the 25th International Conference on Program Comprehension, ICPC 2017, Buenos Aires, Argentina, May 22-23, 2017, pages 263–274, 2017. IEEE Computer Society
Constructing feature model by identifying variability-aware modules [link]Paper   doi   link   bibtex  
StiCProb: A novel feature mining approach using conditional probability. Tang, Y.; and Leung, H. In Pinzger, M.; Bavota, G.; and Marcus, A., editor(s), IEEE 24th International Conference on Software Analysis, Evolution and Reengineering, SANER 2017, Klagenfurt, Austria, February 20-24, 2017, pages 45–55, 2017. IEEE Computer Society
StiCProb: A novel feature mining approach using conditional probability [link]Paper   doi   link   bibtex  
  2015 (1)
Top-down Feature Mining Framework for Software Product Line. Tang, Y.; and Leung, H. In Hammoudi, S.; Maciaszek, L. A.; and Teniente, E., editor(s), ICEIS 2015 - Proceedings of the 17th International Conference on Enterprise Information Systems, Volume 2, Barcelona, Spain, 27-30 April, 2015, pages 71–81, 2015. SciTePress
Top-down Feature Mining Framework for Software Product Line [link]Paper   doi   link   bibtex  
  2012 (1)
Link Prediction Based on Weighted Networks. Yang, Z.; Fu, D.; Tang, Y.; Zhang, Y.; Hao, Y.; Gui, C.; Ji, X.; and Yue, X. In Xiao, T.; Zhang, L.; and Fei, M., editor(s), AsiaSim 2012 - Asia Simulation Conference 2012, Shanghai, China, October 27-30, 2012. Proceedings, Part II, volume 324, of Communications in Computer and Information Science, pages 119–126, 2012. Springer
Link Prediction Based on Weighted Networks [link]Paper   doi   link   bibtex