Institutional Repository
| software defect prediction using fuzzy support vector regression | |
| Yan Zhen; Chen Xinyu; Guo Ping | |
| 2010 | |
| 会议名称 | 7th International Symposium on Neural Networks, ISNN 2010 |
| 会议录名称 | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
| 页码 | 17-24 |
| 会议日期 | 43988 |
| 会议地点 | Shanghai, China |
| 收录类别 | ei |
| 出版地 | Germany |
| 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 |
| 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. |
| 关键词 | Computer Software Selection And Evaluation Defects Forecasting Regression Analysis Vectors |
| 主办者 | Shanghai Jiao Tong University; The Chinese University of Hong Kong; IEEE Shanghai Section; International Neural Network Society; IEEE Computational Intelligence Society |
| 语种 | 英语 |
| 内容类型 | 会议论文 |
| URI标识 | http://ir.iscas.ac.cn/handle/311060/8906 |
| 专题 | 2010软件所会议论文 |
| 推荐引用方式 GB/T 7714 | Yan Zhen,Chen Xinyu,Guo Ping. software defect prediction using fuzzy support vector regression[C]. Germany,2010:17-24. |
| 条目包含的文件 | ||||||
| 文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
| software defect pred(131KB) | 限制开放 | -- | 请求全文 | |||
除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。
修改评论