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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- Korean Institute of Information Scientists and Engineers Journal of Electrical Engineering and Information Science Journal of Electrical Engineering and Information Science Vol.4 No.4
- 발행연도
- 1999.8
- 수록면
- 533 - 538 (6page)
이용수
초록· 키워드
오류제보하기
A variety of statistical software reliability growth models have been developed and utilized. However, since all the statistical models are based on assumptions about the nature of software faults and the stochastic behavior of failure process, there is no single universal model that is applicable for all software projects. Neural networks require only failure data as input and no assumptions are made a priori. This paper considers a neural network approach to software reliability prediction from the failure time data obtained from the software testing. The feedforward network architecture and 4 training regimes are suggested. The cascade-correlation algorithm is used for training and experiments are performed for several data sets collected from different software projects. The experimental results show that feedforward networks adapt well across different softwares and exhibit high accuracy in their predictions.
목차
Abstract
Ⅰ. Introduction
Ⅱ. Related Works and Problems
Ⅲ. Neural Network Implementation
Ⅳ. Prediction Experiments and Results
Ⅴ. Conclusion
References
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