Volume 11 Number 12 (Dec. 2016)
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JSW 2016 Vol.11(12): 1182-1190 ISSN: 1796-217X
doi: 10.17706/jsw.11.12.1182-1190

Rolling Prediction Based Software Reliability Model Consideration with Learning Curve

Tian Jie1, 2*, Wu Ji1, Yang Haiyan1, Liu Chao1
School of Computer Science and Engineering, Bei hang University, Beijing, China.

Abstract—Software reliability is an important factor for evaluating software quality in the domain of safety-critical software. The neural network prediction method has been widely used in reliability prediction area. However, Data noise and other issues make this approach easy to falling into local optimum, and reduce the accuracy of the prediction, it also affect the applicability of the model. In this paper, we consider the learning curve effect, and proposed a neural network based reliability prediction, utilize the rolling forecast method to elevate the accuracy and applicability of neural network. The method is validated through three groups of public data sets. And the results show a fairly accurate prediction capability.

Index Terms—Software reliability, neural network, learning-curve, rolling prediction.


Cite: Tian Jie, Wu Ji, Yang Haiyan, Liu Chao, "Rolling Prediction Based Software Reliability Model Consideration with Learning Curve," Journal of Software vol. 11, no. 12, pp. 1182-1190, 2016.

General Information

ISSN: 1796-217X (Online)
Frequency: Monthly (2006-2019); Bimonthly (Since 2020)
Editor-in-Chief: Prof. Antanas Verikas
Executive Editor: Ms. Yoyo Y. Zhou
Abstracting/ Indexing: DBLP, EBSCO, Google Scholar, ProQuest, INSPEC, ULRICH's Periodicals Directory, WorldCat, etc
E-mail: jsw@iap.org
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