Software Enterprise Risk Detection Model Based on BP Neural Network

Abstract

With the rapid development of software industry, software enterprises have many problems in risk management, and enterprises are facing a huge crisis. In order to detect the risk of software enterprise, a risk detection model based on BP neural network is proposed in this paper. Firstly, risk analysis and modeling are carried out for the software life cycle process of the enterprise. Then, BP neural network is combined with common software risk items to realize supervised deep learning based on label information in data set. Using the advantage of neural network in automatic selection of original data features, the relationship between these risk items is extracted. Meanwhile, the network model in this paper learns the complex relational rules of the input risk features, to classify the risks. Finally, the proposed combined prediction model is compared with other prediction models. The experimental results show that the risk prediction effect of the proposed algorithm is better, to avoid the loss caused by the risk of software enterprises.