Abstract: Software defect prediction has become an important research direction in software testing.The comprehensiveness of defect prediction directly affects the efficiency of testing and program operation.However,the existing defect prediction is based on historical data,and most of them cannot give a reasonable explanation for the prediction process.This black box prediction process only shows the output results,making it difficult for people to know the impact of the internal structure of the test model on the output.In order to solve this problem,it is necessary to select software measurement methods and some typical deep lear-ning models,make a brief comparison of their input,output and structure,analyze them from the two perspectives of the degree of data differences and the processing process of the model on the code,and explain their similarities and differences.Experiments show that the method of deep learning is more effective than traditional software measurement methods in defect prediction,which is mainly caused by their different processing processes of raw data.When using convolution neural network and long-term and short-term memory neural network to predict defects,the data difference is mainly caused by the integrity of the understan-ding of code information.To sum up,in order to improve the prediction ability of software defects,the calculation of the model should comprehensively involve the semantics,logic and context of the code to avoid the omission of useful information.

Key words: Software defect prediction, Explicability, Software metrics, Neural network, Abstract syntax tree