Title :
SNN - A Neural Network Based Combination of Software Reliability Growth Models
Author :
Li, Ang ; Gu, Qing ; Feng, Guang-Cheng ; Chen, Dao-Xu
Author_Institution :
Dept. of Comput. Sci. & Technol., Nanjing Univ., Nanjing, China
Abstract :
Applying SRGMs (Software Reliability Growth Models) to real projects is a major concern in software reliability. Sometimes, it is hard to decide the best model for a specific project. Researchers have made a first step on solving this problem by combination, but the effect was limited in accuracy and adaptability. Aiming to improve the usability of the SRGMs, we propose a neural network based combination method to build accurate and adaptive SNN (Selective Neural Network) model. It avoids relying on a single model, thus reduces the risk to produce inaccurate predictions and improves the average performance in accuracy. Neural network and multi-criteria model selection strategy enable the SNN model to be adapted to various projects, producing accurate predictions. Experiment results show that the SNN model makes a notable improvement in accuracy compared with its component models and other combinational models do.
Keywords :
neural nets; software reliability; adaptive SNN model; multicriteria model selection strategy; neural network based combination method; selective neural network model; software reliability growth models; Computer science; Information science; Laboratories; Mathematical model; Neural networks; Predictive models; Reliability engineering; Software performance; Software reliability; Usability;
Conference_Titel :
Information Science and Engineering (ICISE), 2009 1st International Conference on
Conference_Location :
Nanjing
Print_ISBN :
978-1-4244-4909-5
DOI :
10.1109/ICISE.2009.1068