Embedded Software Reliability Prediction Based on Software Life Cycle

In order to guarantee the quality of embedded software, based on the software life cycle, a BP neural network is proposed to predict the software reliability. First analyze the various factors that affect the reliability of the software, and then select the metrics that affect the reliability of the software based on relevant standards and engineering practices. The software reliability measurement data in the actual project was collected, and the established software reliability prediction model is used to predict the software module defects, and the prediction results are compared with the real results. The comparison results show that the model can effectively predict the number of software module defects and effectively indicate the test key module for the software unit test work.