Software reliability prediction with an improved Elman network model - CHENG Xu-chao1; CHEN Xin-yu1; GUO Ping1 (Tongxin xuebao)
In order to improve accuracy and dependability of using neural network for software reliability prediction,a multi-objective optimization-based improved Elman recurrent network method（Mop-IElman） was proposed.First,on the basis of the Elman network,a self-delay feedback of the output layer as another context layer was designed.Second,the network architecture and the initial outputs of these two context layers were taken as variables of network configuration setting,and NSGA-II was employed to simultaneously optimize prediction performance and robustness,then the Pareto solution was obtained.After that,by maximizing the sum of prediction performance and robustness,the final network configuration setting was determined.Finally,the proposed method was compared with the feed-forward neural network,the Elman network,both the single-objective and the multi-objective optimization Elman networks with respect to two real software failure data.It demonstrated that the proposed Mop-IElman achieves higher prediction accuracy and de-pendability.