SNN - A Neural Network Based Combination of Software Reliability Growth Models
Ang Li, Qing Gu, Guang-Cheng Feng, Daoxu Chen · 2009
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.