My major research interests include machine learning and data mining, especially on software mining. major research interests include machine learning and data mining, especially on software mining. I have served as the area chair of IEEE ICDM, senior PC member of the premium conferences in artificial intelligence such as IJCAI and AAAI, and PC members for other premium conferences such as KDD, NIPS, ICML, etc., and the chair of the International Workshop on Software Mining. I have served as the associate editor (junior) for Frontiers of Computer Science and editorial board member for International Journal of Data Warehousing and Mining. I am the executive board member of ACM SIGKDD China Chapter. I have been granted various awards including the PAKDD Early Career Award, the Excellent Youth Award from NSFC, the New Century Excellent Talents program of the Education Ministry of China, the CCF Distinguished Doctoral Dissertation Award, and Microsoft Fellowship Award, etc.

Research Interest

My current research interests mainly include Machine Learning, Data Mining, especially on Software Mining.

Selected Publications     [Google Scholar]

• S. Wang, L. Huang, A. Gao, J. Ge, T. Zhang, H. Feng, I. Satyarth, M. Li, H. Zhang, and V. Ng. Machine/deep learning for software engineering: A systematic literature review. IEEE Transactions on Software Engineering, 2023, 49(3): 1188-1231.

• H. Sun and M. Li. Enhancing unsupervised domain adaptation by exploiting the conceptual consistency of multiple self-supervised tasks. Science China: Information Sciences, 2023, 66(4):142101.

• Y.-F. Ma and M. Li. The flowing nature matters: feature learning from the control flow graph of source code for bug localization. Machine Learning, 2022, 111(3):853-870.

• X. Huo, F. Thung, M. Li, D. Lo, and S.-T. Shi. Deep transfer bug localization. IEEE Transactions on Software Engineering, 2021, 47(7):1368-1380.

• Y.-F. Ma, Y. Du, and M. Li. Capturing the long-distance dependency in the control flow graph via structural-guided attention for bug localization. In: Proceedings of the 32nd International Joint Conference on Artificial Intelligence (IJCAI'23), Macau, China, 2023.

• Z. Xie, H. Sun, and M. Li. Semi-supervised learning with support isolation by small-paced self-training. In: Proceedings of the 37th AAAI Conference on Artificial Intelligence (AAAI'23), Washington, DC, 2023.

• H. Sun, Z. Xie, X.-Y. Li and M. Li. Cooperative and adversarial learning: Co-enhancing discriminability and transferability in domain adaptation. In: Proceedings of the 37th AAAI Conference on Artificial Intelligence (AAAI'23), Washington, DC, 2023.

• Y. Lyu, T. Le Cong, H. Kang, R. Widyasari, Z. Zhao, B. Le, M. Li, and D. Lo. CHRONOS: Time-aware zero-shot identification of libraries from vulnerability reports. In: Proceedings of 45th IEEE/ACM International Conference on Software Engineering (ICSE'23), Melbourne, Australia, 2023.

• L. Chai and M. Li. Pyramid attention for source code summarization. In: Advances in Neural Information and Processing Systems 35 (NeurIPS'22), New Orleans, LA, 2022, 20421-20433.

• Y. Fan and M. Li. Towards generating summaries for lexically confusing code through code erosion. In: Proceedings of the 30th International Joint Conference on Artificial Intelligence (IJCAI'21), Montreal, Canada, 2021, pp. 3721-3727.

• X. Huo, M. Li, and Z.-H. Zhou. Control flow graph embedding based on multi-instance decomposition for bug localization. In: Proceedings of the 34th AAAI Conference on Artificial Intelligence (AAAI'20), New York, NY, 2020.

• S.-T. Shi, W. Zheng, J. Tang, Q.-G. Chen, Y. Hu, J. Zhu, and M. Li. Deep time-stream framework for click-through rate prediction by tracking interest evolution. In: Proceedings of the 34th AAAI Conference on Artificial Intelligence (AAAI'20), New York, NY, 2020.

• Y.-Y. Zhang and M. Li. Find me if you can: Deep software clone detection by exploiting the contest between the plagiarist and the detector. In: Proceedings of the 33rd AAAI Conference on Artificial Intelligence (AAAI'19), Honolulu, HI, 2019.

• S.-T. Shi, M. Li,, D. Lo, F. Thung, and X. Huo. Automatic code review by learning the revision of source code. In: Proceedings of the 33rd AAAI Conference on Artificial Intelligence (AAAI'19), Honolulu, HI, 2019.

• Z.-Y. Shen and M. Li. T2S: Domain adaptation via model-independent inverse mapping and model reuse. In: Proceedings of the 18th International Conference on Data Mining (ICDM'18), Singapore, 2018, pp.1224-1229.

• Z. Xie and M. Li. Cutting the software building efforts in continuous integration by semi-supervised online AUC optimization. In: Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI'18), Stockholm, Sweden, 2018, pp.2875-2881.

• H.-H. Wei and M. Li. Positive and unlabeled learning for detecting software functional clones with adversarial training. In: Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI'18), Stockholm, Sweden, 2018, pp.2840-2846.

• Z. Xie and M. Li. Semi-supervised AUC optimization without guessing labels of unlabeled data. In: Proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI'18), New Orleans, LA, 2018, pp.4310-4317.

• X. Huo and M. Li. Enhancing the unified features to locate buggy files by exploiting the sequential nature of source code. In: Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI'17), Melbourne, Australia, 2017, 1909-1915.

• H.-H. Wei and M. Li. Supervised deep features for software functional clone detection by exploiting lexical and syntactical information in source code. In: Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI'17), Melbourne, Australia, 2017, 3034-3040.

• X. Huo, M. Li, and Z.-H. Zhou. Learning unified features from natural and programming languages for locating buggy source code. In: Proceedings of the 25th International Joint Conference on Artificial Intelligence (IJCAI'16), New York, NY, 2016, 1606-1612. 

• M. Li, H. Zhang, R. Wu, and Z.-H. Zhou. Sample-based software defect prediction with active and semi-supervised learning. Automated Software Engineering, 2012, 19(2): 201-230.

Last modified: Dec. 27， 2024