Dr. Yutao Ma's homepage
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Jan. 2025 One paper was accepted by ACM TOSEM.
Dec. 2024 One paper was accepted by IEEE ICASSP 2025, one paper was published by Elsevier CMIG, and two papers were presented in IEEE BIBM 2024.
Nov. 2024 Congratulations to Qingbin Wang for defending his PhD dissertation.
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.
- Siladittya Manna, Saumik Bhattacharya, Umapada Pal. Self-Supervised Visual Representation Learning for Medical Image Analysis: A Comprehensive Survey. Transactions on Machine Learning Research, 2024.
- 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.
- Sanne Vermorgen, Thijs Gelton, Peter Bult, et al. Endometrial Pipelle Biopsy Computer-Aided Diagnosis: A Feasibility Study. Modern Pathology, 2024, 37(2): 100417.
- Clare McGenity, Emily L. Clarke, Charlotte Jennings, et al. Artificial intelligence in digital pathology: a systematic review and meta-analysis of diagnostic test accuracy. npj Digital Medicine, 2024, 7: 114.
- 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.
- Sheetal Prusty, Lingraj Dora, Rutuparna Panda, Sanjay Agrawal, Ajith Abraham. Molecular imaging analysis in cancer using deep learning: a review. _ Research on Biomedical Engineering _, 2023, 39: 1015–1032.
- 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.
- Philippa Kadama-Makanga, Aggrey Semeere, Miriam Laker-Oketta, et al. Usability of a smartphone-compatible, confocal micro-endoscope for cervical cancer screening in resource-limited settings. BMC Women’s Health, 2024, 24: 483.
- Chiho Yoon, Eunwoo Park, Donggyu Kim, Byullee Park, and Chulhong Kim. Label-free optical microscopy with artificial intelligence: a new paradigm in pathology. Biophotonics Discovery, 2025, 2(2): 020901.
- 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.
- Zilong Wang, Jiaying Yu, Wenli Zhu, et al. Unveiling the mysteries of extrachromosomal circular DNA: from generation to clinical relevance in human cancers and health. Molecular Cancer, 2024, 23: 276.
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.
- Thomas Klein and Robert Huber. High-speed OCT light sources and systems. Biomedical Optics Express, 2017, 8(2): 828-859.
- 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.
Hadeel Alhosaini, Sultan Alharbi, Xianzhi Wang, Guandong Xu. API Recommendation For Mashup Creation: A Comprehensive Survey. The Computer Journal, 2024, 67(5): 1920–1940.
- 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.
- Zhu Sun, Yu Lei, Lu Zhang, Chen Li, Yew-Soon Ong, Jie Zhang. A Multi-channel Next POI Recommendation Framework with Multi-granularity Check-in Signals. ACM Transactions on Information Systems, 2024, 42(1): 15.
- Wenjie Fu, Huandong Wang, Chen Gao, Guanghua Liu, Yong Li, Tao Jiang. Privacy-Preserving Individual-Level COVID-19 Infection Prediction via Federated Graph Learning. ACM Transactions on Information Systems, 2024, 42(3): 82.
- Haolin Zhou, Junwei Pan, Xinyi Zhou, Xihua Chen, Jie Jiang, Xiaofeng Gao, Guihai Chen. Temporal Interest Network for User Response Prediction. In Proceedings of the 33rd ACM Web Conference (WWW 2024), Companion Volume, pp. 413-422, 2024.
- Chao Chen, Haoyu Geng, Nianzu Yang, Xiaokang Yang, Junchi Yan. EasyDGL: Encode, Train and Interpret for Continuous-Time Dynamic Graph Learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024, 46(12): 10845-10862.
- Hao Wang, Mingjia Yin, Luankang Zhang, Sirui Zhao, Enhong Chen. MF-GSLAE: A Multi-Factor User Representation Pre-Training Framework for Dual-Target Cross-Domain Recommendation. ACM Transactions on Information Systems, 2025, 43(2): 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.
- Zhu Sun, Yu Lei, Lu Zhang, Chen Li, Yew-Soon Ong, Jie Zhang. A Multi-channel Next POI Recommendation Framework with Multi-granularity Check-in Signals. ACM Transactions on Information Systems, 2024, 42(1): 15.
- Qianru Zhang, Haixin Wang, Cheng Long, Liangcai Su, Xingwei He, Jianlong Chang, Tailin Wu, Hongzhi Yin, Siu-Ming Yiu, Qi Tian, Christian S. Jensen. A Survey of Generative Techniques for Spatial-Temporal Data Mining. CoRR, arXiv 2405.09592, 2024.
- Chen Wang, Mengting Yuan, Yang Yang, Kai Peng, Hongbo Jiang. Revisiting Long- and Short-Term Preference Learning for Next POI Recommendation With Hierarchical LSTM. IEEE Transactions on Mobile Computing, 2024, 23(12): 12693-12705.
- 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.
- 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.
- Ke Sun, Tieyun Qian, Chenliang Li, Xuan Ma, Qing Li, Ming Zhong, Yuanyuan Zhu, Mengchi Liu. Pre-Training Across Different Cities for Next POI Recommendation. ACM Transactions on the Web, 2023, 17(4): 31.
- 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.
- Yuanyuan Zhou, Zhijun Ding, Changjun Jiang (蒋昌俊院士). Service Workflow Activity Input/Output Parameters Recommendation Method by Combining Transformer and Weighted HITS. IEEE Transactions on Services Computing, 2024, 17(6): 4296-4309.
- 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.
- Chao Ni, Xin Xia, David Lo, Xiang Chen, Qing Gu. Revisiting Supervised and Unsupervised Methods for Effort-Aware Cross-Project Defect Prediction. IEEE Transactions on Software Engineering, 2022, 48(3): 786-802.
- Bruno Góis Mateus, Matias Martinez, Christophe Kolski. Learning migration models for supporting incremental language migrations of software applications. Information and Software Technology, 2023, 153: 107082.
- 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.
- Iyad Alazzam, Ahmed Aleroud, Zainab Al Latifah, George Karabatis. Automatic Bug Triage in Software Systems Using Graph Neighborhood Relations for Feature Augmentation. IEEE Transactions on Computational Social Systems, 2020, 7(5): 1288-1303.
- 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.
- Markus Borg, Leif Jonsson, Emelie Engström, Béla Bartalos, Attila Szabó. Adopting automated bug assignment in practice — a longitudinal case study at Ericsson. Empirical Software Engineering, 2024, 29: 126.
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.
- 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, 2024, 33(4): 98:1-98:31.
- Mohammad Jamil Ahmad, Katerina Goseva-Popstojanova, Robyn R. Lutz. The untold impact of learning approaches on software fault-proneness predictions: an analysis of temporal aspects. Empirical Software Engineering, 2024, 29: 87.
- Giammaria Giordano, Gerardo Festa, Gemma Catolino, Fabio Palomba, Filomena Ferrucci, Carmine Gravino. On the adoption and effects of source code reuse on defect proneness and maintenance effort. Empirical Software Engineering, 2024, 29: 20.
- 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.
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| 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 | |
| 2018 | Qingbin Wang | Second-class Scholarship (2019, 2023) One IEEE/ACM TCBB paper One MICCAI 2024 paper |
Wuhan University (Postdoctoral Fellow) | |
| 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 | |
| 2021 | Zesheng Wang | Second-class Scholarship (2022) One ACM TWEB paper |
ByteDance | |
| 2021 | Weijun Huang | Second-class Scholarship (2023) |
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: 2025-01-28