Zou, Yanzhen

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Zou, Yanzhen

Associate Professor

Research Interests: Software reuse, software data mining, software Q&A

Office Phone: 86-10-6275 1794-15

Email: zouyz@pku.edu.cn

Zou, Yanzhen is an associate professor in the Department of Computer Science and technology, School of EECS. She obtained his B.Sc. from JiLin University in 2002, and Ph.D. from Peking University in 2010 respectively. Her research interests include software engineering, software reuse, software data mining and natural language based software question & answer.

Dr. Zou has published more than 40 research papers, and most of them are published in international conferences and journals, such as ASE, ICSME and ICWS. She has applied and obtained 6 software patents of China. She has participated or led more than 10 national projects, such as the Key Project of the National Eleventh Five-Year Research Program of China, Hi-Tech Research and Development Program of China, The National Natural Science Foundation of China, and so on.

Dr. Zou has investigated a range of problems surrounding software reuse, including Internet based Software Reuse, Large-scale software repository, Natural Language based Software Question & Answer, etc. Her research achievements are summarized as follows:

-  Internet based software reuse & Large-scale software repository

Software reuse is a practical way to improve the efficiency of software development. I believe that by mining and learning from legacy software and open source software, understanding how people create, explore, evolve, and reason about software systems, we can enhance developers' effectiveness and improve the quality of their systems. We have put forward a serious of approaches to discover reusable software resources on the Internet. These software resources are measured, classified, and now available at our large-scale software repository TSR (http://tsr.trustie.net/).

-  Natural Language based Software Question & Answer.

With the expansion of the software data scale, a challenge in software artifact reuse is to locate the right answer for a developer’s question. To deal with this issue, we investigated question understanding and reformulation approaches in software text retrieval system further. Considering different interrogatives usually indicate users’ different search requirement, we built interrogative based documents classification mechanism and improved the precision of software retrieval. At the same time, we investigated how to recover traceability links between code and different kinds of software documents. Our targets are to build software project oriented knowledge graph and implement knowledge graph based software question and answer system.