Research On Model-based Code Generation Technology And Software Reliability

With the rapid iterative development of software development technology,and the increasing scale of software and the increasing demand for reliability,research on software automation generation and reliability prediction technology has become a hot issue in the field of software engineering.There are many software reliability metrics available,including code-based metrics and text-based metrics.Relevant research shows that the most concentrated stage of software failure is the design and implementation phase of software.The traditional software syst em analysis,design and development process based on document or manual code is difficult to fully acquire the complex process data and information of the system.Therefore,traditional software reliability metrics have great limitations in the case of insufficient data and knowledge sets.This topic is based on graphical model-driven and code automatic generation technology.Firstly,a graphical model-driven software design and code automatic generation system is designed and implemented.The code generati on framework and algorithm based on class diagram,state diagram and structured flowchart are given.Secondly,three kinds of data sets based on model software development process are established.Based on XML technology implements the storage structure and stored procedures of data extraction.Furthermore,the existing software reliability metrics and the calculation conditions and calculation process of various indicators are studied.Finally,based on the three types of data sets and the extracted metric s,the software reliability prediction research is carried out.Based on the analysis of the existing machine learning algorithms,the optimal algorithm is selected for reliability prediction.The paper tests the code generation system.The test results show that all the functions of the code generation system designed and implemented in this paper have reached the design goal.The data set and indicators extracted by this method are compared with the reliability prediction algorithm based on this data.The re is a big improvement in the way.