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Title:
software defect prediction using fuzzy support vector regression
Author: Yan Zhen ; Chen Xinyu ; Guo Ping
Source: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Conference Name: 7th International Symposium on Neural Networks, ISNN 2010
Conference Date: 43988
Issued Date: 2010
Conference Place: Shanghai, China
Keyword: Computer software selection and evaluation ; Defects ; Forecasting ; Regression analysis ; Vectors
Publish Place: Germany
Indexed Type: ei
ISSN: 3029743
ISBN: 3642133177
Department: (1) School of Computer, Beijing Institute of Technology, Beijing 100081, China; (2) State Key Laboratory of Computer Science, Institute of Software, Chinese Academy of Sciences, Beijing 100190, China
Sponsorship: Shanghai Jiao Tong University; The Chinese University of Hong Kong; IEEE Shanghai Section; International Neural Network Society; IEEE Computational Intelligence Society
English Abstract: Regression techniques have been applied to improve software quality by using software metrics to predict defect numbers in software modules. This can help developers allocate limited developing resources to modules containing more defects. In this paper, we propose a novel method of using Fuzzy Support Vector Regression (FSVR) in predicting software defect numbers. Fuzzification input of regressor can handle unbalanced software metrics dataset. Compared with the approach of support vector regression, the experiment results with the MIS and RSDIMU datasets indicate that FSVR can get lower mean squared error and higher accuracy of total number of defects for modules containing large number of defects. © 2010 Springer-Verlag.
Language: 英语
Content Type: 会议论文
URI: http://ir.iscas.ac.cn/handle/311060/8906
Appears in Collections:中科院软件所图书馆_2010软件所会议论文

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Recommended Citation:
Yan Zhen,Chen Xinyu,Guo Ping. software defect prediction using fuzzy support vector regression[C]. 见:7th International Symposium on Neural Networks, ISNN 2010. Shanghai, China. 43988.
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