软件可靠性设计技术应用研究
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摘要
计算机技术的高速发展，致使武器装备系统和自动化指挥系统等军用系统对软件的依赖程度越来越高。软件在武器装备、航天航海等要求高可靠性的系统中扮演着越来越重要的角色。因此，军用软件可靠性就成为确保军事系统质量的瓶颈和关键。
软件可靠性是软件质量中最为重要的一项属性，软件可靠性设计技术是确保和提高软件质量的重要手段。因此，本文以工程项目的软件作为主要研究对象，开展软件可靠性设计技术应用研究。依据软件研制周期，先从可靠性设计的三个方面入手，软件可靠性设计要求、软件可靠性详细设计、确定代码编写规范；然后通过收集故障数据开展可靠性增长预计模型的研究。论文主要研究内容与成果如下：
第一，深入研究软件可靠性设计方法及软件可靠性设计的重要准则。在软件需求分析和概要设计阶段提出针对项目软件的可靠性设计要求。
第二，在故障模式、影响和危害度分析(Failure Mode, Effects and CriticalityAnalysis, FMECA)和质量功能展开(Quality Function Deployment, QFD)的基础上，提出了系统级软件可靠性屋(House of Software Reliablity, HoSR)模型。在详细设计阶段，建立通用性的故障模式及故障原因库并不断扩充故障模式及故障原因库。基于该库再利用HoSR模型对软件单元模块进行可靠性分析，找出潜在故障模式及引发该故障的原因，并可直观得到共模故障、共因故障，也可得到故障模式之间的关联性以及故障原因之间的关联性，从而控制模块自身缺陷，控制模块之间的故障传递以及控制传递过来的缺陷和模块自身缺陷的共同作用导致的缺陷。最后根据故障原因提出故障改进措施。通过该分析结果指导可靠性设计，以提高软件可靠性。
第三，采用智能优化算法结合动态模糊神经网络(Dynamic Fuzzy NeuralNetwork, DFNN)的方法，使用仿预测手段得到DFNN自身参数的最优值，建立了软件可靠性增长预测模型(Software Reliability Growth Prediction Model,SRGPM)。在软件验收前期，使用DFNN建立软件可靠性预测模型，使用软件缺陷数据对DFNN训练并进行仿预测，在仿预测过程中使用智能优化算法求取DFNN自身参数的最优值。然后再用训练好的DFNN对软件可靠性进行预测，与G-O模型、BP神经网络、模糊神经网络(Fuzzy Neural Network, FNN)建立的SRGPM相比，预测误差小并且稳定性高。
With the rapid development of computer technology, weaponry systems andautomated command systems and other military systems depend much more onsoftware than before. Software plays an increasingly important role in weaponry,space, navigation and other systems which need high reliability. Therefore, militarysoftware reliability becomes bottleneck and key factor of military system.
Software reliability is the most important attribute of software quality. Softwarereliability design technology is an important means to ensure and improve softwarequality. Therefore, this dissertation takes the project’s software as main object ofstudy, the application of software reliability design technology is carried out. Basedon the software development cycle, we can start reliability design from three aspects,which are software reliability design requirements, detailed software reliabilitydesign, code written specification. And then failure data are collected, andexperiments and assesses software reliability growth are performed The maincontributions of this dissertation are described as follows:
Firstly, the in-depth studies of software reliability design methods and softwarereliability design criteria are performed. At the stage of software requirementsanalysis and outline design stage, software reliability design requirements for theproject software should be proposed.
Secondly, based on the Failure Mode, Effects and Criticality Analysis (FMECA)and Quality Function Deployment (QFD), system-level house of software reliability(HoSR) model is presented. At the stage of detailed design stage, a common failuremode and causes of failure library should be established, and it should be constantlyfilled. Based on the library, HoSR is used to analyze software reliability of unitmodule, which help us find potential failure modes and the causes of failure.Common mode failures, common cause failure, the relationships between the failuremodes and the relationships between the causes of failure can be intuitively found,thus module itself defect can be controlled. The failure of transmission between themodules can be controlled, and passed defect can be controlled. Subsequently, defectby the coaction of module itself defect can be controlled. Finally, improvementscould be proposed based on the causes of failure. Reliability design could be guidedby the results of the analysis, for the result that software reliability could beimproved.
Thirdly, intelligent optimization algorithm is combined with dynamic fuzzyneural network (DFNN), and the optimal value of DFNN parameters is gotten byimitative prediction (IP), accordingly, software reliability growth prediction model isestablished. Before acceptance of software, software reliability prediction model isestablished by DFNN. DFNN is trained by defect data and IP is proceeded. Duringthe IP, intelligent optimization algorithm is used to get the optimal value of DFNNparameters. And then DFNN which has been trained is used to predict softwarereliability, compared with G-O model, BP neural network and fuzzy neural network,its prediction error is small and steady.
软件可靠性是软件质量中最为重要的一项属性，软件可靠性设计技术是确保和提高软件质量的重要手段。因此，本文以工程项目的软件作为主要研究对象，开展软件可靠性设计技术应用研究。依据软件研制周期，先从可靠性设计的三个方面入手，软件可靠性设计要求、软件可靠性详细设计、确定代码编写规范；然后通过收集故障数据开展可靠性增长预计模型的研究。论文主要研究内容与成果如下：
第一，深入研究软件可靠性设计方法及软件可靠性设计的重要准则。在软件需求分析和概要设计阶段提出针对项目软件的可靠性设计要求。
第二，在故障模式、影响和危害度分析(Failure Mode, Effects and CriticalityAnalysis, FMECA)和质量功能展开(Quality Function Deployment, QFD)的基础上，提出了系统级软件可靠性屋(House of Software Reliablity, HoSR)模型。在详细设计阶段，建立通用性的故障模式及故障原因库并不断扩充故障模式及故障原因库。基于该库再利用HoSR模型对软件单元模块进行可靠性分析，找出潜在故障模式及引发该故障的原因，并可直观得到共模故障、共因故障，也可得到故障模式之间的关联性以及故障原因之间的关联性，从而控制模块自身缺陷，控制模块之间的故障传递以及控制传递过来的缺陷和模块自身缺陷的共同作用导致的缺陷。最后根据故障原因提出故障改进措施。通过该分析结果指导可靠性设计，以提高软件可靠性。
第三，采用智能优化算法结合动态模糊神经网络(Dynamic Fuzzy NeuralNetwork, DFNN)的方法，使用仿预测手段得到DFNN自身参数的最优值，建立了软件可靠性增长预测模型(Software Reliability Growth Prediction Model,SRGPM)。在软件验收前期，使用DFNN建立软件可靠性预测模型，使用软件缺陷数据对DFNN训练并进行仿预测，在仿预测过程中使用智能优化算法求取DFNN自身参数的最优值。然后再用训练好的DFNN对软件可靠性进行预测，与G-O模型、BP神经网络、模糊神经网络(Fuzzy Neural Network, FNN)建立的SRGPM相比，预测误差小并且稳定性高。
With the rapid development of computer technology, weaponry systems andautomated command systems and other military systems depend much more onsoftware than before. Software plays an increasingly important role in weaponry,space, navigation and other systems which need high reliability. Therefore, militarysoftware reliability becomes bottleneck and key factor of military system.
Software reliability is the most important attribute of software quality. Softwarereliability design technology is an important means to ensure and improve softwarequality. Therefore, this dissertation takes the project’s software as main object ofstudy, the application of software reliability design technology is carried out. Basedon the software development cycle, we can start reliability design from three aspects,which are software reliability design requirements, detailed software reliabilitydesign, code written specification. And then failure data are collected, andexperiments and assesses software reliability growth are performed The maincontributions of this dissertation are described as follows:
Firstly, the in-depth studies of software reliability design methods and softwarereliability design criteria are performed. At the stage of software requirementsanalysis and outline design stage, software reliability design requirements for theproject software should be proposed.
Secondly, based on the Failure Mode, Effects and Criticality Analysis (FMECA)and Quality Function Deployment (QFD), system-level house of software reliability(HoSR) model is presented. At the stage of detailed design stage, a common failuremode and causes of failure library should be established, and it should be constantlyfilled. Based on the library, HoSR is used to analyze software reliability of unitmodule, which help us find potential failure modes and the causes of failure.Common mode failures, common cause failure, the relationships between the failuremodes and the relationships between the causes of failure can be intuitively found,thus module itself defect can be controlled. The failure of transmission between themodules can be controlled, and passed defect can be controlled. Subsequently, defectby the coaction of module itself defect can be controlled. Finally, improvementscould be proposed based on the causes of failure. Reliability design could be guidedby the results of the analysis, for the result that software reliability could beimproved.
Thirdly, intelligent optimization algorithm is combined with dynamic fuzzyneural network (DFNN), and the optimal value of DFNN parameters is gotten byimitative prediction (IP), accordingly, software reliability growth prediction model isestablished. Before acceptance of software, software reliability prediction model isestablished by DFNN. DFNN is trained by defect data and IP is proceeded. Duringthe IP, intelligent optimization algorithm is used to get the optimal value of DFNNparameters. And then DFNN which has been trained is used to predict softwarereliability, compared with G-O model, BP neural network and fuzzy neural network,its prediction error is small and steady.
引文
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