ISCAS OpenIR  > 2010软件所会议论文
software defect prediction using fuzzy support vector regression
Yan Zhen; Chen Xinyu; Guo Ping
2010
Conference Name7th International Symposium on Neural Networks, ISNN 2010
SourceLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages17-24
Conference Date43988
Conference PlaceShanghai, China
Indexed Typeei
Publish PlaceGermany
ISSN3029743
ISBN3642133177
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
English AbstractRegression 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.
KeywordComputer Software Selection And Evaluation Defects Forecasting Regression Analysis Vectors
SponsorshipShanghai Jiao Tong University; The Chinese University of Hong Kong; IEEE Shanghai Section; International Neural Network Society; IEEE Computational Intelligence Society
Language英语
Content Type会议论文
URIhttp://ir.iscas.ac.cn/handle/311060/8906
Collection2010软件所会议论文
Recommended Citation
GB/T 7714
Yan Zhen,Chen Xinyu,Guo Ping. software defect prediction using fuzzy support vector regression[C]. Germany,2010:17-24.
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