| 题名: | software defect prediction using fuzzy support vector regression |
| 作者: | Yan Zhen
; Chen Xinyu
; Guo Ping
|
| 会议文集: | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
|
| 会议名称: | 7th International Symposium on Neural Networks, ISNN 2010
|
| 会议日期: | 43988
|
| 出版日期: | 2010
|
| 会议地点: | Shanghai, China
|
| 关键词: | Computer software selection and evaluation
; Defects
; Forecasting
; Regression analysis
; Vectors
|
| 出版地: | Germany
|
| 收录类别: | ei
|
| ISSN: | 3029743
|
| ISBN: | 3642133177
|
| 部门归属: | (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
|
| 主办者: | Shanghai Jiao Tong University; The Chinese University of Hong Kong; IEEE Shanghai Section; International Neural Network Society; IEEE Computational Intelligence Society
|
| 英文摘要: | 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. |
| 语种: | 英语
|
| 内容类型: | 会议论文
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| URI标识: | 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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| software defect prediction using fuzzy support vector regression.pdf（131KB） | -- | -- | 限制开放 | -- | 联系获取全文 |
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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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