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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Li Ruan Yongji Wang Qing Wang Fengdi Shu Haitao Zeng Shen Zhang |
| Copyright Year | 2006 |
| Description | Author affiliation: Inst. of Software, Chinese Acad. of Sci., Beijing (Li Ruan; Yongji Wang; Qing Wang; Fengdi Shu; Haitao Zeng; Shen Zhang) |
| Abstract | Productivity is a critical performance index of process resources. As successive history productivity data tends to be auto-correlated, time series prediction method based on auto-regressive integrated moving average (ARIMA) model was introduced into software productivity prediction by Humphrey et al. In this paper, a variant of their prediction method named ARIMAmmse is proposed. This variant formulates the ARIMA parameter estimation issue as a minimum mean square error (MMSE) based constrained optimization problem. The ARIMA model is used to describe constraints of the parameter estimation problem, while MMSE is used as the objective function of the constrained optimization problem. According to the optimization theory, ARIMAmmse will definitely achieve a higher MMSE prediction precision than Humphrey et al's which is based on the Yule-Walk estimation technique. Two comparative experiments are also presented. The experimental results further confirm the theoretical superiority of ARIMAmmse |
| Sponsorship | IEEE CPS |
| Starting Page | 135 |
| Ending Page | 138 |
| File Size | 152134 |
| Page Count | 4 |
| File Format | |
| ISBN | 0769526551 |
| ISSN | 07303157 |
| DOI | 10.1109/COMPSAC.2006.115 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2006-09-17 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Productivity Parameter estimation Prediction methods Constraint optimization Autocorrelation Software performance Mean square error methods Software quality Laboratories Performance analysis |
| Content Type | Text |
| Resource Type | Article |
| Subject | Computer Science Applications Software |
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