Dr. Yutao Ma's homepage
¦ Background ¦ Services ¦ Awards ¦ Funding ¦ Publications ¦ Teaching ¦ Students ¦ Contact ¦
Jan. 2024 One patent of AI-assisted diagnosis for cervical OCT images was issued.
Nov. 2023 Congratulations to group members who won their scholarship.
Oct. 2023 We won the grand prize in the 18th “Challenge Cup” National Undergraduate Curricular Academic Science and Technology Works by Race.
Yutao Ma (马于涛) is a Full Professor in the School of Computer Science at Central China Normal University. Dr. Ma was previously an Associate Professor in the School of Computer Science at Wuhan University and the deputy director of the Institute of Intelligentized Software and Services in Wuhan University’s School of Computer Science. He is a member of the Software Service Engineering and Application Laboratory. In 2007, Dr. Ma received his Ph.D. degree in Computer Science from Wuhan University. He worked as a postdoctoral researcher with Prof. Deyi Li in the Institute of China Electronic System Engineering Corporation and as a short-term visiting scholar in the Department of Electrical and Computer Engineering at Lehigh University. In 2016, he began leading a scientific and technical innovation team in Hubei Province. Besides, he has also been selected for the Wuhan Yellow Crane Talent Programme and the Wuhan Youth ChengGuang Programme of Science and Technology. His research interests include the development and maintenance of intelligent software services, such as service-based system development using artificial intelligence approaches, software repository mining, and domain-specific application and practice (especially in biomedical imaging and computer-aided diagnosis).
I have received a few notable international and domestic awards or prizes, including
My research has been supported by the National Basic Research Program of China (973 Program), the Natural Science Foundation of China (NSFC), and many other research funds. Selected national funds include:
For more publications, please refer to my profile on ResearchGate, DBLP, and Google Scholar.
Here # denotes who contributed equally to the work, and * means the corresponding author.
Wei-Chien Wang, Euijoon Ahn, Dagan Feng, and Jinman Kim. A Review of Predictive and Contrastive Self-supervised Learning for Medical Images. Machine Intelligence Research, 2023, 20(4): 483-513.
- Abtin Riasatian, Morteza Babaie, Danial Maleki, et al. Fine-Tuning and training of densenet for histopathology image representation using TCGA diagnostic slides. Medical Image Analysis, 2021, 70: 102032.
- Munetoshi Akazawa and Kazunori Hashimoto. Artificial intelligence in gynecologic cancers: Current status and future challenges – A systematic review. Artificial Intelligence in Medicine, 2021, 120: 102164.
- Bas H.M.van der Velden, Hugo J. Kuijf, Kenneth G.A. Gilhuijs, and Max A. Viergever. Explainable artificial intelligence (XAI) in deep learning-based medical image analysis. Medical Image Analysis, 2022, 79: 102470.
- Mahnaz Mohammadi, Jessica Cooper, Ognjen Arandelović, et al. Weakly supervised learning and interpretability for endometrial whole slide image diagnosis. Experimental Biology and Medicine, 2022, 247(22): 2025-2037.
- Emma Rewcastle, Einar Gudlaugsson, Melinda Lillesand, Ivar Skaland, Jan P.A. Baak, and Emiel A.M. Janssen. Automated Prognostic Assessment of Endometrial Hyperplasia for Progression Risk Evaluation Using Artificial Intelligence. Modern Pathology, 2023, 36(5): 100116.
- Xiang Li, Minglei Li, Pengfei Yan, Guanyi Li, Yuchen Jiang, Hao Luo, and Shen Yin. Deep Learning Attention Mechanism in Medical Image Analysis: Basics and Beyonds. International Journal of Network Dynamics and Intelligence, 2023, 2(1): 93–116.
- Md Imran Hossain, Ghada Zamzmi, Peter R. Mouton, Md Sirajus Salekin, Yu Sun, and Dmitry Goldgof. Explainable AI for Medical Data: Current Methods, Limitations, and Future Directions. ACM Computing Surveys, DOI: 10.1145/3637487, 2023
- Yifeng Zeng, Shiqi Xu, William C. Chapman, Shuying Li, Zahra Alipour, Heba Abdelal, Deyali Chatterjee, Matthew Mutch, and Quing Zhu. Real-time colorectal cancer diagnosis using PR-OCT with deep learning. In Proceedings of the 2020 Conference on Optical Coherence Tomography (OCT 2020), Washington, DC United States, 20–23 April, 2020, p. OW2E.5.
- Rebecca Richards-Kortum, Cesaltina Lorenzoni, Vanderlei S. Bagnato, and Kathleen Schmeler. Optical imaging for screening and early cancer diagnosis in low-resource settings. Nature Reviews Bioengineering, 2024, 2: 25-43.
- Kathryn A. Kundrod, Chelsey A. Smith, Brady Hunt, Richard A. Schwarz, Kathleen Schmeler, and Rebecca Richards-Kortum. Advances in technologies for cervical cancer detection in low-resource settings. Expert Review of Molecular Diagnostics, 2019, 19(8): 695-714.
- Xingchen Ji, Diana Mojahed, Yoshitomo Okawachi, Alexander L. Gaeta, Christine P. Hendon, and Michal Lipson. Millimeter-scale chip–based supercontinuum generation for optical coherence tomography. Science Advances, 2021, 7(38): eabg8869.
- Jackson B. Coole, David Brenes, Júlio César Possati-Resende, Márcio Antoniazzi, Bruno de Oliveira Fonseca, Yajur Maker, Alex Kortum, Imran S. Vohra, Richard A. Schwarz, Jennifer Carns, Karen Cristina Borba Souza, Iara Viana Vidigal Santana, Regis Kreitchmann, Mila P. Salcedo, Nirmala Ramanujam, Kathleen M. Schmeler, and Rebecca Richards-Kortum. Development of a multimodal mobile colposcope for real-time cervical cancer detection. Biomedical Optics Express, 2022, 13(10): 5116-5130.
- Lukas Glandorf, Paul-James Marchand, Theo Lasser, and Daniel Razansky. Digital aberration correction enhances field of view in visible-light optical coherence microscopy. Optics Letters, 2022, 47(19): 5088-5091.
- Rebecca Richards-Kortum, Cesaltina Lorenzoni, Vanderlei S. Bagnato, and Kathleen Schmeler. Optical imaging for screening and early cancer diagnosis in low-resource settings. Nature Reviews Bioengineering, 2024, 2: 25-43.
Shang Wang, Irina V. Larina, and Kirill V. Larin. Label-free optical imaging in developmental biology. Biomedical Optics Express, 2020, 11(4): 2017-2040.
- Hee Yoon Lee, Helge Sudkamp, Tahereh Marvdashti, and Audrey K. Ellerbee. Interleaved optical coherence tomography. Optics Express, 2013, 21(22): 26542-26556.
- Trevor Anderson, Armin Segref, Grant Frisken, and Steven Frisken. 3D spectral imaging system for anterior chamber metrology. Proceedings of SPIE, 2015, 9312: 93120N.
- Biwei Yin, Jordan Dwelle, Bingqing Wang, Tianyi Wang, Marc D. Feldman, Henry G. Rylander, and Thomas E. Milner. Fourier optics analysis of phase-mask-based path-length-multiplexed optical coherence tomography. Journal of the Optical Society of America A, 2015, 32(11): 2169-2177.
- Pablo Eugui, Antonia Lichtenegger, Marco Augustin, Danielle J. Harper, Stanislava Fialová, Andreas Wartak, Christoph K. Hitzenberger, and Bernhard Baumann. Few-mode fiber detection for tissue characterization in optical coherence tomography. Proceedings of SPIE, 2017, 10416: 104160M.
- Shaozhen Song, Jingjiang Xu, and Ruikang K. Wang. Flexible wide‐field optical micro‐angiography based on Fourier‐domain multiplexed dual‐beam swept source optical coherence tomography. Journal of Biophotonics, 2018, 11(3): e201700203.
- Jungho Moon, Yong-Sik Lim, Seokchan Yoon, and Wonshik Choi. Single-shot multi-depth full-field optical coherence tomography using spatial frequency division multiplexing. Optics Express, 2021, 29(5): 7060-7069.
- Denzel Faulkner, Marien Ochoa, Navid Ibtehaj Nizam, Shan Gao, and Xavier Intes. Diffuse Fluorescence Tomography. In book: Biomedical Optical Imaging: From Nanoscopy to Tomography, Chapter 11. AIP Publishing, Melville, New York, 2021.
- Min Woo Lee, Namseon Jang, Nara Choi, et al. In Vivo Cellular-Level 3D Imaging of Peripheral Nerves Using a Dual-Focusing Technique for Intra-Neural Interface Implantation. Advanced Science, 2022, 9(3): 2102876.
- Karol Karnowski, Jadwiga Milkiewicz, Angela Pachacz, Andrea Curatolo, Onur Cetinkaya, Rafal Pietruch, Piotr Ciacka, Ashkan Eliasy, Ahmed Abass, Ahmed Elsheikh, Susana Marcos, and Maciej Wojtkowski. Air puff-coupled multi-spot OCT for assessment of asymmetries in corneal biomechanics. Proceedings of SPIE, 2022, 11941: 119410U.
- Aleksandr Petrov and Craig Macdonald. A Systematic Review and Replicability Study of BERT4Rec for Sequential Recommendation. In Proceedings of the Sixteenth ACM Conference on Recommender Systems (RecSys 2022), pp. 436-447, 2022.
- Zhiwei Guo, Keping Yu, Ali Kashif Bashir, Di Zhang, Yasser D. Al-Otaibi, and Mohsen Guizani. Deep Information Fusion-Driven POI Scheduling for Mobile Social Networks. IEEE Network, 2022, 36(4): 210-216.
- Jingyu Gao, Chaohuan Hou (候朝焕院士), and Sancong Ying. DraG4Rec: A Symmetrical View of User Preference and Item Popularity. TechRxiv, 21540315, 2022.
- Jingyu Gao, Chaohuan Hou (候朝焕院士), and Sancong Ying. Edge-view Dual Message Carriers GCN for Sequential Recommendation. TechRxiv, 22291393, 2023.
- Haoyang Li, Ziwei Zhang, Xin Wang, and Wenwu Zhu. Invariant Node Representation Learning under Distribution Shifts with Multiple Latent Environments. ACM Transactions on Information Systems, 2024, 42(1): 26:1-26:30.
- Jiayuan He, Jianzhong Qi, and Ramamohanarao Kotagiri. TimeSAN: A Time-Modulated Self-Attentive Network for Next Point-of-Interest Recommendation. In Proceedings of the 2020 IEEE International Joint Conference on Neural Networks (IJCNN 2020), pp. 1-8, 2020.
- Yudong Chen, Xin Wang, Miao Fan, Jizhou Huang, Shengwen Yang, and Wenwu Zhu. Curriculum Meta-Learning for Next POI Recommendation. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining (KDD 2021), pp. 2692–2702, 2021.
- Chenwang Zheng, Dan Tao, Jiangtao Wang, Lei Cui, Wenjie Ruan, and Shui Yu. Memory Augmented Hierarchical Attention Network for Next Point-of-Interest Recommendation. IEEE Transactions on Computational Social Systems, 2021, 8(2): 489-499.
- Zhiwei Guo, Keping Yu, Ali Kashif Bashir, Di Zhang, Yasser D. Al-Otaibi, and Mohsen Guizani. Deep Information Fusion-Driven POI Scheduling for Mobile Social Networks. IEEE Network, 2022, 36(4): 210-216.
- Zhiwei Guo, Keping Yu, Neeraj Kumar, Wei Wei, Shahid Mumtaz, and Mohsen Guizani. Deep-Distributed-Learning-Based POI Recommendation Under Mobile-Edge Networks. IEEE Internet of Things Journal, 2023, 10(1): 303-317.
- Jiaqi Wu, Jingyi Yuan, Yang Weng, and Raja Ayyanar. Spatial-Temporal Deep Learning for Hosting Capacity Analysis in Distribution Grids. IEEE Transactions on Smart Grid, 2023, 14(1): 354-364.
- Chen Wang, Mengting Yuan, Rui Zhang, Kai Peng, and Ling Liu. Efficient Point-of-Interest Recommendation Services with Heterogenous Hypergraph Embedding. IEEE Transactions on Services Computing, 2023, 16(2): 1132-1143.
- Hanrui Wu, Chung Wang Wong, Jia Zhang, Yuguang Yan, Dahai Yu, Jinyi Long, and Michael Ng. Cold-start Next-item Recommendation by User-Item Matching and Auto-encoders. IEEE Transactions on Services Computing, 2023, 16(4): 2477-2489.
- Jing Chen, Wenjun Jiang, Jie Wu, Kenli Li, and Keqin Li. Dynamic Personalized POI Sequence Recommendation With Fine-grained Contexts. ACM Transactions on Internet Technology, 2023, 23(2): 32:1-32:28.
- Yan Luo, Ye Liu, Fu-lai Chung, Yu Liu, and Changwen Chen. End-to-End Personalized Next Location Recommendation via Contrastive User Preference Modeling. Computing Research Repository (CoRR), arXiv:2303.12507, 2023.
- Junjie Ou, Haiming Jin, Xiaocheng Wang, Hao Jiang, Xinbing Wang, and Chenghu Zhou (周成虎院士). STA-TCN: Spatial-Temporal Attention over Temporal Convolutional Network for Next Point-of-Interest Recommendation. ACM Transactions on Knowledge Discovery from Data, 2023, 17(9): 124:1-124:19.
- Zhaobo Wang, Yanmin Zhu, Chunyang Wang, Wenze Ma, Bo Li, and Jiadi Yu. Adaptive Graph Representation Learning for Next POI Recommendation. In Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2023), pp. 393–402, 2023.
- Jinze Wang, Lu Zhang, Zhu Sun, and Yew-Soon Ong. Meta-learning Enhanced Next POI Recommendation by Leveraging Check-ins from Auxiliary Cities. In Proceedings of the 27th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2023), pp. 322-334, 2023.
Junpei Masuho, Tomo Miyazaki, Yoshihiro Sugaya, Masako Omachi, and Shinichiro Omachi. A Framework for Estimating Gaze Point Information for Location-Based Services. IEEE Transactions on Vehicular Technology, 2021, 70(9): 8468-8477.
- Kaushik Madala, Shraddha Piparia, Eduardo Blanco, Hyunsook Do, and Renee Bryce. Model elements identification using neural networks: a comprehensive study. Requirements Engineering, 2021, 26: 67–96.
- Michael Fisher, Viviana Mascardi, Kristin Yvonne Rozier, Bernd-Holger Schlingloff, Michael Winikoff, and Neil Yorke-Smith. Towards a framework for certification of reliable autonomous systems. Autonomous Agents and Multi-Agent Systems, 2021, 35: 8:1-8:65.
- Unil Yun, Heonho Kim, Taewoog Ryu, Yoonji Baek, Hyoju Nam, Judae Lee, Bay Vo, and Witold Pedrycz. Pre-Large based Utility-Oriented Data Analytics for Transaction Modifications in Internet of Things. IEEE Internet of Things Journal, 2021, 8(24): 17333-17344.
- Chao Liu, Xin Xia, David Lo, Zhiwe Liu, Ahmed E. Hassan, and Shanping Li. CodeMatcher: Searching Code Based on Sequential Semantics of Important Query Words. ACM Transactions on Software Engineering and Methodology, 2022, 31(1): 12:1-12:37.
- Haozhe Lin, Yushun Fan, Jia Zhang, Bing Bai, Zhenghua Xu, and Thomas Lukasiewicz. Toward Knowledge as a Service (KaaS): Predicting Popularity of Knowledge Services Leveraging Graph Neural Networks. IEEE Transactions on Services Computing, 2023, 16(1): 642-655.
- Ignacio Lizarralde, Cristian Mateos, Alejandro Zunino, Tim A. Majchrzak, and Tor-Morten Grønli. Discovering web services in social web service repositories using deep variational autoencoders. Information Processing & Management, 2020, 57(4): 102231.
- Lei Sang, Min Xu, Shengsheng Qian, and Xindong Wu. Knowledge graph enhanced neural collaborative recommendation. Expert Systems with Applications, 2021, 164: 113992.
- Haiyang Zhang, Ivan Ganchev, Nikola S. Nikolov, Zhanlin Ji, and Mairtin O’Droma. FeatureMF: An Item Feature Enriched Matrix Factorization Model for Item Recommendation. IEEE Access, 2021, 9: 65266-65276.
- Zhen Wang, Chang-Ai Sun, and Marco Aiello. Context-aware IoT Service Recommendation: A Deep Collaborative Filtering-based Approach. In Proceedings of the 29th IEEE International Conference on Web Services (ICWS 2022), pp. 150-159, 2022.
- Neha Agarwal, Geeta Sikka, and Lalit Kumar Awasthi. A systematic literature review on web service clustering approaches to enhance service discovery, selection and recommendation. Computer Science Review, 2022, 45: 100498.
- Javier Criado and Luis Iribarne. Reusability and discovery models in software systems: a systematic literature review. Journal of Object Technology, 2022, 21(4): 3.
- Le Sun, Rui Zhou, Dandan Peng, Athman Bouguettaya, and Yanchun Zhang. Automatically Building Service-Based Systems With Function Relaxation. IEEE Transactions on Cybernetics, 2023, 53(5): 2703-2716.
Mehdi Bahrami and Mukesh Singhal. DCCSOA: A Dynamic Cloud Computing Service-Oriented Architecture. In Proceedings of the 2015 IEEE International Conference on Information Reuse and Integration (IEEE IRI 2015), pp. 158-165, 2015.
- Dengguo Feng (冯登国院士), Min Zhang, and Hao Li. Big Data Security and Privacy Protection. Chinese Journal of Computers 2014, 37(1): 246-258. (in Chinese with English Abstract)
- Arezou Soltani Panah, Ron G. van Schyndel, Timos K. Sellis, and Elisa Bertino. On the Properties of Non-Media Digital Watermarking: A Review of State of the Art Techniques. IEEE Access, 2016, 4: 2670-2704.
Naresh KumarNagwani and Jasjit S.Surib. An artificial intelligence framework on software bug triaging, technological evolution, and future challenges: A review. International Journal of Information Management Data Insights, 2023, 3(1): 100153.
Sourabh Pal and Alberto Sillitti. Cross-Project Defect Prediction: A Literature Review. IEEE Access, 2022, 10: 118697-118717.
- Shengqu Xi, Yuan Yao, Xusheng Xiao, Feng Xu, and Jian Lu (吕建院士). Bug Triaging Based on Tossing Sequence Modeling. Journal of Computer Science and Technology, 2019, 34(5): 942-956. (Conference Version)
- Ali Sajedi Badashian and Eleni Stroulia. Investigating the information value of different sources of evidence of developers’ expertise for bug assignment in open-source projects. IET Software, 2020, 14(7): 748-758.
- Steffen Herbold, Aynur Amirfallah, Fabian Trautsch, and Jens Grabowski. A systematic mapping study of developer social network research. Journal of Systems and Software, 2021, 171: 110802.
Yuming Zhou, Yibiao Yang, Hongmin Lu, et al. How Far We Have Progressed in the Journey? An Examination of Cross-Project Defect Prediction. ACM Transactions on Software Engineering and Methodology, 2018, 27(1): 1:1-1:51.
- Pradeep Singh, Nikhil R. Pal, Shrish Verma, and Om Prakash Vyas. Fuzzy Rule-Based Approach for Software Fault Prediction. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2017, 47(5): 826-837.
- Steffen Herbold, Alexander Trautsch, and Jens Grabowski. A Comparative Study to Benchmark Cross-Project Defect Prediction Approaches. IEEE Transactions on Software Engineering, 2018, 44(9): 811-833.
- Fabian Trautsch, Steffen Herbold, Philip Makedonski, and Jens Grabowski. Addressing problems with replicability and validity of repository mining studies through a smart data platform. Empirical Software Engineering, 2018, 23(2): 1036-1083.
- Seyedrebvar Hosseini, Burak Turhan, and Dimuthu Gunarathna. A Systematic Literature Review and Meta-Analysis on Cross Project Defect Prediction. IEEE Transactions on Software Engineering, 2019, 45(2): 111-147.
- Fangyun Qin, Zheng Zheng, Yu Qiao, and Kishor S. Trivedi. Studying Aging-Related Bug Prediction Using Cross-Project Models. IEEE Transactions on Reliability, 2019, 68(3): 1134-1153.
- Santosh S. Rathore and Sandeep Kumar. A study on software fault prediction techniques. Artificial Intelligence Review, 2019, 51: 255–327.
- Qinbao Song, Yuchen Guo, and Martin J. Shepperd. A Comprehensive Investigation of the Role of Imbalanced Learning for Software Defect Prediction. IEEE Transactions on Software Engineering, 2019, 45(12): 1253-1269.
- Christopher Theisen and Laurie Williams. Better together: Comparing vulnerability prediction models. Information and Software Technology, 2020, 119: 106204.
- Ke Li, Zilin Xiang, Tao Chen, Shuo Wang, and Kay Chen Tan. Understanding the Automated Parameter Optimization on Transfer Learning for Cross-Project Defect Prediction: An Empirical Study. In Proceedings of the 42nd International Conference on Software Engineering (ICSE 2020), Seoul, South Korea, June 27 – July 19, 2020, pp. 566-577.
- Mojdeh Golagha, Alexander Pretschner, and Lionel C. Briand. Can We Predict the Quality of Spectrum-based Fault Localization? In Proceedings of the 13th IEEE International Conference on Software Testing, Validation and Verification (ICST 2020), Porto, Portugal, 24-28 October, 2020, pp. 4-15.
- Sousuke Amasaki, Hirohisa Aman, and Tomoyuki Yokogawa. A Preliminary Evaluation of CPDP Approaches on Just-in-Time Software Defect Prediction. In Proceedings of the 47th Euromicro Conference on Software Engineering and Advanced Applications (SEAA 2021), Palermo, Italy, 1-3 September, 2021, pp. 279-286.
- Jiayuan Zhou, Shaowei Wang, Cor-Paul Bezemer, Ying Zou, and Ahmed E. Hassan. Studying the Association between Bountysource Bounties and the Issue-addressing Likelihood of GitHub Issue Reports. IEEE Transactions on Software Engineering, 2021, 47(12): 2919-2933.
- Sousuke Amasaki, Hirohisa Aman, and Tomoyuki Yokogawa. An extended study on applicability and performance of homogeneous cross-project defect prediction approaches under homogeneous cross-company effort estimation situation. Empirical Software Engineering, 2022, 27: 46.
- Yanming Yang, Xin Xia, David Lo, Tingting Bi, John Grundy, and Xiaohu Yang. Predictive Models in Software Engineering: Challenges and Opportunities. ACM Transactions on Software Engineering and Methodology, 2022, 31(3): 56.
- Jinfu Chen, Weiyi Shang, and Emad Shihab. PerfJIT: Test-level Just-in-time Prediction for Performance Regression Introducing Commits. IEEE Transactions on Software Engineering, 2022, 48(5): 1529-1544.
- Steffen Tunkel and Steffen Herbold. Exploring the relationship between performance metrics and cost saving potential of defect prediction models. Empirical Software Engineering, 2022, 27: 182.
- Mohammad Jamil Ahmad, Katerina Goseva-Popstojanova, and Robyn R. Lutz. The Untold Impact of Learning Approaches on Software Fault-Proneness Predictions. Computing Research Repository (CoRR), arXiv:2207.05710, 2022.
- Bruno Góis Mateus, Matias Martinez, and Christophe Kolski. Learning migration models for supporting incremental language migrations of software applications. Information and Software Technology, 2023, 153: 107082.
- Adil Mukhtar, Birgit Hofer, Dietmar Jannach, and Franz Wotawa. Explaining software fault predictions to spreadsheet users. Journal of Systems and Software, 2023, 201: 111676.
- Zhaoqiang Guo, Shiran Liu, Xutong Liu, et al. Code-line-level bugginess identification: How far have we come, and how far have we yet to go? ACM Transactions on Software Engineering and Methodology, 2023, 32(4): 102:1-102:55.
- N. C. Shrikanth and Tim Menzies. Assessing the Early Bird Heuristic (for Predicting Project Quality). ACM Transactions on Software Engineering and Methodology, 2023, 32(5): 116:1–116:39.
- Shaiful Chowdhury, Gias Uddin, Hadi Hemmati, and Reid Holmes. Method-Level Bug Prediction: Problems and Promises. ACM Transactions on Software Engineering and Methodology, DOI: 10.1145/3640331, 2024.
- Karl R. Weiss, Taghi M. Khoshgoftaar, and Dingding Wang. A survey of transfer learning. Journal of Big Data, 2016, 3: Art. no. 9.
- Steffen Herbold, Alexander Trautsch, and Jens Grabowski. Global vs. local models for cross-project defect prediction - A replication study. Empirical Software Engineering, 2017, 22(4): 1866-1902.
- Yarong Zeng, Yue Yu, Qiang Fan, Xunhui Zhang, Tao Wang, Gang Yin, and Huaimin Wang (王怀民院士). Cross-Project Issue Classification Based on Ensemble Modeling in a Social Coding World. In Proceedings of the 25th International Conference on Neural Information Processing (ICONIP 2018), Part IV, pp. 281-292, 2018.
- Yuming Zhou, Yibiao Yang, Hongmin Lu, et al. How Far We Have Progressed in the Journey? An Examination of Cross-Project Defect Prediction. ACM Transactions on Software Engineering and Methodology, 2018, 27(1): 1:1-1:51.
- Jaechang Nam, Wei Fu, Sunghun Kim, Tim Menzies, and Lin Tan. Heterogeneous Defect Prediction. IEEE Transactions on Software Engineering, 2018, 44(9): 874-896. (Conference Version)
- Seyedrebvar Hosseini, Burak Turhan, and Dimuthu Gunarathna. A Systematic Literature Review and Meta-Analysis on Cross Project Defect Prediction. IEEE Transactions on Software Engineering, 2019, 45(2): 111-147.
- Sousuke Amasaki. Cross-version defect prediction: use historical data, cross-project data, or both? Empirical Software Engineering, 2020, 25(2): 1573-1595.
- Suvodeep Majumder, Tianpei Xia, Rahul Krishna, and Tim Menzies. Methods for Stabilizing Models Across Large Samples of Projects (with case studies on Predicting Defect and Project Health). In Proceedings of the IEEE/ACM 19th International Conference on Mining Software Repositories (MSR 2022), Pittsburgh, PA, USA, May 23-24, 2022, pp. 566-578.
- Pravas Ranjan Bal and Sandeep Kumar. A Data Transfer and Relevant Metrics Matching based Approach for Heterogeneous Defect Prediction. IEEE Transactions on Software Engineering, 2023, 49(3): 1232-1245.
- Yixiu Kong, Guiyuan Shi, Ruijie Wu, and Yicheng Zhang. k-core: Theories and applications. Physics Reports, 2019, 832: 1-32.
- Yu Qu, Qinghua Zheng (郑庆华院士), Jianlei Chi, et al. Using K-core Decomposition on Class Dependency Networks to Improve Bug Prediction Model’s Practical Performance. IEEE Transactions on Software Engineering, 2021, 47(2): 348-366.
- Qusay Alsarhan, Bestoun S. Ahmed, Miroslav Bures, and Kamal Zuhairi Zamli. Software Module Clustering: An In-Depth Literature Analysis. IEEE Transactions on Software Engineering, 2022, 48(6): 1905-1928.
- Yu Qu, Xiaohong Guan (管晓宏院士), Qinghua Zheng (郑庆华院士), et al. Exploring community structure of software Call Graph and its applications in class cohesion measurement. Journal of Systems and Software, 2015, 108: 193-210.
- Michael Aram and Gustaf Neumann. Multilayered analysis of co-development of business information systems. Journal of Internet Services and Applications, 2015, 6(1): 13:1-13:30.
- An Zeng, Zhesi Shen, Jianlin Zhou, Jinshan Wu, Ying Fan, Yougui Wang, and H. Eugene Stanley. The science of science: From the perspective of complex systems. Physics Reports, 2017, 714–715: 1-73.
- Marçal Mora-Cantallops, Salvador Sánchez-Alonso, and Elena García-Barriocanal. A complex network analysis of the Comprehensive R Archive Network (CRAN) package ecosystem. Journal of Systems and Software, 2020, 170: 110744.
- Drew A. Vecchio, Samuel H. Mahler, Mark D. Hammig, and Nicholas A. Kotov. Structural Analysis of Nanoscale Network Materials Using Graph Theory. ACS Nano, 2021, 15(8): 12847-12859.
- Ivana Turnu, Giulio Concas, Michele Marchesi, and Roberto Tonelli. The fractal dimension of software networks as a global quality metric. Information Sciences, 2013, 245: 290-303.
- Sara Abbaspour Asadollah, Daniel Sundmark, Sigrid Eldh, Hans Hansson, and Eduard Paul Enoiu. A Study of Concurrency Bugs in an Open Source Software. In Proceedings of the 12th IFIP WG 2.13 International Conference on Open Source Systems: Integrating Communities (OSS 2016), pp. 16-31, 2016.
- An Zeng, Zhesi Shen, Jianlin Zhou, Jinshan Wu, Ying Fan, Yougui Wang, and H. Eugene Stanley. The science of science: From the perspective of complex systems. Physics Reports, 2017, 714–715: 1-73.
陈关荣(Guanrong Chen). 复杂网络及其新近研究进展简介. 力学进展, 2008, 38(6): 653-662. (in Chinese with English abstract)
- Davy Landman, Alexander Serebrenik, Eric Bouwers, and Jurgen J. Vinju. Empirical analysis of the relationship between CC and SLOC in a large corpus of Java methods and C functions. Journal of Software: Evolution and Process, 2016, 28(7): 589-618.
- Gábor Szárnyas, Zsolt Kővári, Ágnes Salánki, and Dániel Varró. Towards the characterization of realistic models: evaluation of multidisciplinary graph metrics. In Proceedings of the 19th ACM/IEEE International Conference on Model Driven Engineering Languages and Systems (MODELS 2016), pp. 87-94, 2016.
- Srdjan Stevanetic and Uwe Zdun. Supporting the analyzability of architectural component models - empirical findings and tool support. Empirical Software Engineering, 2018, 23(6): 3578-3625.
- Andrea Capiluppi, Nemitari Ajienka, and Steve Counsell. The effect of multiple developers on structural attributes: A Study based on java software. Journal of Systems and Software, 2020, 167: 110593.
- Stefano Dalla Palma, Chiel van Asseldonk, Gemma Catolino, Dario Di Nucci, Fabio Palomba, and Damian A. Tamburri. “Through the looking-glass …” An empirical study on blob infrastructure blueprints in the Topology and Orchestration Specification for Cloud Applications. Journal of Software: Evolution and Process, 10.1002/smr.2533, 2023.
- Reuven Cohen and Shlomo Havlin. Complex Networks: Structure, Robustness and Function. Cambridge University Press, 2010.
- Craig Taube-Schock, Robert J. Walker, and Ian H. Witten. Can We Avoid High Coupling?. In Proceedings of the 25th European Conference on Object-Oriented Programming (ECOOP 2011), pp. 204-228, 2011.
- Miloš Savić, Mirjana Ivanović, and Lakhmi C. Jain. Analysis of Software Networks. In Complex Networks in Software, Knowledge, and Social Systems, pp. 59-141, 2019.
- Luciano da Fontoura Costa, Osvaldo N. Oliveira Jr., Gonzalo Travieso, et al. Analyzing and modeling real-world phenomena with complex networks: a survey of applications. Advances in Physics, 2011, 60(3): 329-412.
- Pamela Bhattacharya, Marios Iliofotou, Iulian Neamtiu, and Michalis Faloutsos. Graph-based analysis and prediction for software evolution. In Proceedings of the 34th International Conference on Software Engineering (ICSE 2012), pp. 419-429, 2012.
- Gabriel Ferreira, Momin M. Malik, Christian Kästner, Jürgen Pfeffer, and Sven Apel. Do #ifdefs influence the occurrence of vulnerabilities? an empirical study of the linux kernel. In Proceedings of the 20th International Systems and Software Product Line Conference (SPLC 2016), pp. 65-73, 2016.
- Christos Ellinas, Neil Allan, and Anders Johansson. Exploring Structural Patterns Across Evolved and Designed Systems: A Network Perspective. Systems Engineering, 2016, 19(3): 179-192.
| Grade | Name | Award or Scholarship | Employment |
|---|---|---|---|
| 2008 | Weifeng Pan (Ph.D. Supervisor: Prof. Bing Li) |
Second-class Scholarship (2010) | Zhejiang Gongshang University |
| 2012 | Peng He (Ph.D. Supervisor: Prof. Bing Li) |
National Scholarship for postgraduates (2013, 2014) | Hubei University |
| 2013 | Hongrun Wu (Ph.D. Supervisor: Prof. Deyi Li) |
One-year exchange student at Rovira i Virgili University Short-term visiting student at Indiana University |
Minnan Normal University |
| 2013 | Qing Xu | Short-term visiting student at Lehigh University | Guotai Junan Securities |
| 2014 | Mingming Chen | One-year visiting student at Lehigh University National Scholarship for postgraduates (2016) |
NetEase |
| 2014 | Guoan You | Best Paper Award of SEKE 2016 Special Scholarship (2016) |
Baidu |
| 2014 | Haiyang Liu | China UnionPay | |
| 2015 | Tao Xu | One-year visiting student at Lehigh University Second-class Scholarship (2017) One IEEE TBME paper |
Ultralucia |
| 2015 | Xiaowan Shi | Second-class Scholarship (2017) | JD.COM |
| 2015 | Huazhi Song | JD.COM | |
| 2016 | Junchao Liu | One-year visiting student at Lehigh University One issued patent (ZL201910542267.6) |
Huawei |
| 2017 | Ye Liu | Sina | |
| 2017 | Yiwei Huang | Huawei | |
| 2017 | Shengnan Ding | Second-class Scholarship (2017, 2018) | Xiaomi |
| 2017 | Feng Wang | CCF Award for Outstanding Undergraduates (2017) Entrance Scholarship (2017) Second-class Scholarship (2018, 2019) |
ByteDance |
| 2017 | Jinxiao Huang | Special Scholarship (2019) Second-class Scholarship (2018, 2019) |
Xiaomi |
| 2018 | Hao Sun | Special Scholarship (2020) First-class Scholarship (2019, 2020) One IEEE JBHI paper |
Tencent |
| 2018 | Xiao Geng | One IEEE TEM paper One issued patent (ZL202011359895.X) One CAAI TRIT paper |
Kuaishou |
| 2019 | Qinyi Yu | Second-class Scholarship (2020) One issued patent (ZL202110268171.2) |
Tencent |
| 2019 | Siqin Yang | China Construction Bank Corporation | |
| 2019 | Wanrong Dou | Entrance Scholarship (2019) First-class Scholarship (2020) One issued patent (ZL202011343939.X) |
National Centre for Computer Network and Information Security Management |
| 2020 | Qi Sun | Huawei | |
| 2020 | Kaiyi Chen | Entrance Scholarship (2020) Second-class Scholarship (2021) Runner-up in the China Postgraduate AI Innovation Competition (2021) National Scholarship for postgraduates (2022) First-class Scholarship (2022) Grand Prize in the 18th “Challenge Cup” National Undergraduate Curricular Academic Science and Technology Works by Race (2023) One issued patent (ZL202110778854.2) |
ByteDance |
| 2021 | Xiaoyang Chen | Second-class Scholarship (2022) | Baidu |
Mail: Room 7097, School of Computer Science, Central China Normal University, Wuhan 430079, Hubei Province, China
E-mail: ytma@ccnu.edu.cn or ytma@whu.edu.cn
Last updated: 2024-01-16