多维在线跨语言Calling Network建模及其在可信国家电子税务软件中的实证应用

项目名称： 多维在线跨语言Calling Network建模及其在可信国家电子税务软件中的实证应用

项目编号： No.91418205

项目类型： 重大研究计划

立项/批准年度： 2015

项目学科： 自动化技术、计算机技术

项目作者： 郑庆华

作者单位： 西安交通大学

项目金额： 170万元

中文摘要： 针对国家电子税务软件新出现的可信需求：用户行为可信监控和系统版本演化可信评测，在现有软件调用网络模型Calling Network（CN）和软件可信评测与控制技术研究基础上，持续研究面向网络软件的MOC-CN模型，实现软件行为监控从单一系统离线建模，到多维（Multi-dimension）、在线（Online）、跨语言（Cross-language）建模的提升；挖掘软件行为与用户行为的模式特征和映射关系，研究软件和用户异常行为的实时监测和识别方法；研究基于MOC-CN的软件版本演化差异检测和差异引导的可信评测方法。以个人所得税征管系统为载体，研制电子税务软件可信监控和评测系统，在陕西、河北、安徽、广西、上海等9个省市开展示范应用，支持90万纳税人的行为监管，并协助税务部门维护软件升级3个月，参与国家金税工程三期电子税务软件行业规范的制定，在此基础上形成可信软件理论方法的典型应用案例。

中文关键词： 可信软件；国家电子税务软件；软件调用网络；用户行为建模；软件版本演化差异检测

英文摘要： This project is conducted considering two new trustworthy requirements of national E-taxation software, namely user behavior trustworthy monitoring and trustworthy evaluation in software evolution. In our previous research, a model named as Calling Network (CN) was proposed to describe a software system’s runtime method call behaviors and structures. Software trustworthy evaluation and testing techniques based on CN model were also proposed. In this project, an advanced online CN model, named as MOC-CN, is studied to describe Multi-dimension, Cross-language interactive relations between software entities at different granularities. Software and user behavior patterns and their mapping relationship are investigated. Abnormal detection techniques on software and user behaviors are explored. Based on MOC-CN, the software behavior-based difference detection method is examined to find the effective differences among various versions of software, which is applied to guiding test case generation in software trustworthy testing. The trustworthy monitoring and evaluation systems will be developed for the individual income taxation systems in nine provinces, including Shaanxi, Hebei, Anhui, Guangxi, Shanghai etc. The trustworthy monitoring systems are deployed to monitor more than 900,000 users; and the evaluation systems

英文关键词： Trustworthy software；National E-taxation software；Calling Network；User behavior modeling；Difference detection in software evolution