Chunru Wang

Work place: Faculty of Automation, GuangDong University of Technology, Guangzhou, China



Research Interests: Computer Networks, Network Architecture, Process Control System


Chunru Wang, was born in 1968, received the PhD in Control Theory and Control Engineering in South China University of Technology (Guangzhou, China) in 2009. She is a lecturer of Faculty of Automation, GuangDong University of Technology (Guangzhou, China), teaching Digital Logic, PLC and Control Network. She has published several papers in international conferences and journals in her major research fields, some of which are indexed by EI. Her current research interests control network and WSN.

Author Articles
Data Mining based Software Development Communication Pattern Discovery

By Gang Zhang Caixian ye Chunru Wang Xiaomin He

DOI:, Pub. Date: 8 Dec. 2010

Smaller time loss and smoother communication pattern is the urgent pursuit in the software development enterprise. However, communication is difficult to control and manage and demands on technical support, due to the uncertainty and complex structure of data appeared in communication. Data mining is a well established framework aiming at intelligently discovering knowledge and principles hidden in massive amounts of original data. Data mining technology together with shared repositories results in an intelligent way to analyze data of communication in software development environment. We propose a data mining based algorithm to tackle the problem, adopting a co-training styled algorithm to discover pattern in software development environment. Decision tree is trained as based learners and a majority voting procedure is then launched to determine labels of unlabeled data. Based learners are then trained again with newly labeled data and such iteration stops when a consistent state is reached. Our method is naturally semi-supervised which can improve generalization ability by making use of unlabeled data. Experimental results on data set gathered from productive environment indicate that the proposed algorithm is effective and outperforms traditional supervised algorithms.

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