基于PSOABC-SVM的软件可靠性预测模型

软件可靠性预测是指在软件开发初期对软件中各模块出错的可能性进行预测，对提高软件的可信性具有重要意义。提出了一种基于粒子群与人工蜂群优化支持向量机的软件可靠性预测模型，将粒子群优化算法与人工蜂群算法相结合的混合算法引入到支持向量机的参数选择中，提高软件可靠性预测的效果。实验结果表明，该模型比BP网络预测模型、粒子群优化支持向量机等预测模型收敛速度更快、预测精度更高，能更好的进行软件可靠性预测。

Software Reliability Prediction Based on PSOABC-SVM Model

Software reliability prediction can predict the fault-prone modules at the early age of sofrware development. And it is important to improve the credibility of the software. In order to improve the effect of software reliability prediction, this paper proposes a PSOABC-SVM model to predict software reliability, and puts forward a model of predicting the software reliability based on PSOABC-SVM. The experimental results show that this model can achieve more precise prediction results than other prediction models such as BP neural network and PSO-SVM. The PSOABC-SVM model is more applicable for software reliability prediction.