Caixian ye

Work place: IT Department, NiuTaiLai communication equipment Co.Ltd., GuangZhou , China

E-mail: yecaixian@tom.com

Website:

Research Interests: Computational Science and Engineering, Computational Engineering, Data Structures and Algorithms, Engineering

Biography

Caixian Ye, was born in 1980, Guangzhou, received the Master’s Degree in software engineering in HuaZhong University of Science and technology (Wuhan, China) in 2007. She is an IT project director of NiuTaiLai Communication Equipment Co.Ltd. (Guangzhou, China)´╝îin charge of system analysis, project management, data engineering, project resource arrangement and product / solution design. She is experienced in Java Web development, such as JSP, Servlet and EJB. She is also professional in common database systems such as Oracle, DB2, Sybase and SQL Server.

Author Articles
Application Research on Data Mining Methods in Information Communication Mode of Software Development

By Caixian ye Gang Zhang

DOI: https://doi.org/10.5815/ijeme.2012.05.12, Pub. Date: 29 May 2012

Smaller time loss and smoother information communication mode is the urgent pursuit of the software R&D enterprise. Information communication is difficult to control and manage and it needs more technical to support. Data mining is an intelligent way tried to analyze knowledge and laws which hidden in massive amounts of data. Data mining technology together with share repositories can improve the intelligent degree of information communication mode. In this paper, the framework of intelligent information communication mode which based on data mining technology and share repositories is advanced, and data mining model for information communication of software development is designed. In view of the extant single decision tree algorithm existence the characteristics that counting inefficient and its learning based on supervise, a new semi-supervised learning algorithm three decision trees voting classification algorithm based on tri-training (TTVA) is proposed. This algorithm in training only requests a few labeled data, and can use massively unlabeled data repeatedly revision to the classifier. It has overcome the single decision tree algorithm shortcoming. Experiments on the real communicated data sets of software developmental item indicate that TTVA has the good identification and accuracy to the crux issues mining, and can apply to the decision analysis of the development and management of the software project. At the same time, TTVA can effectively exploit the massively unlabeled data to enhance the learning performance.

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Data Mining based Software Development Communication Pattern Discovery

By Gang Zhang Caixian ye Chunru Wang Xiaomin He

DOI: https://doi.org/10.5815/ijmecs.2010.02.04, 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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