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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Hu, Q.P. Dai, Y.S. Xie, M. Ng, S.H. |
| Copyright Year | 2006 |
| Description | Author affiliation: Dept. of Ind. & Syst. Eng., National Univ. of Singapore (Hu, Q.P.) |
| Abstract | Generally, software reliability models can provide accurate reliability measurement in the later phase of testing. However, predictions in the early phase of software testing are useful as cost-effective and timely feedback. Early prediction is also feasible in practice with information from previous releases or similar projects. Such information has been utilized well for early reliability prediction with NHPP models by assuming the same failure rate between two similar projects. Alternatively, in this paper, we propose to "reuse" failure data from past projects/releases with ANN models to improve early reliability for current project/release. To illustrate the proposed approach, two numerical examples are developed. Better prediction performance is observed in early phase of testing compared with original ANN model without failure data reuse. Furthermore, the optimal switching point from proposed approach to original ANN model in the whole testing phase is studied, with specific analysis on the two examples |
| Sponsorship | IEEE CPS |
| Starting Page | 234 |
| Ending Page | 239 |
| File Size | 141759 |
| Page Count | 6 |
| File Format | |
| ISBN | 0769526551 |
| ISSN | 07303157 |
| DOI | 10.1109/COMPSAC.2006.130 |
| 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 | Software reliability Predictive models Software testing Artificial neural networks Process control Resource management Software measurement Reliability engineering Data analysis Information analysis |
| Content Type | Text |
| Resource Type | Article |
| Subject | Computer Science Applications Software |
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