舰艇装备软件可靠性测试方法及预测研究
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摘要
舰艇装备软件的可靠程度对我军现役舰艇作战生存率有重大影响,并且对其服役期限的确定有重要作用。随着军队现代化水平的不断提高,军队武器自动化程度呈现了前所未有的进步和发展,现代化军队武器装备系统作为一个大型的软件密集型系统,软件的稳定、可靠直接关系着军队装备的成败。目前国内海上靶场针对舰载武备系统软件专项试验尚属起步阶段,软件可靠性测试尚未列入验收项目目录,靶场还没有建立起完善的软件可靠性测试和预测方法。为全面准确地对新一代舰艇武备系统考核,论文作者结合多年海上试验经验,参与制定了适应于新装备发展的舰艇装备软件可靠性测试和试验方法并且研究了其可靠性预测手段,为舰艇装备软件的验收工作提供方法和标准。
论文在介绍软件可靠性和软件可靠性工程的基础上,分析了多种常用的软件可靠性模型,研究了传统可靠性预测技术。传统的可靠性预测方法大多是基于软件故障时间的,论文将软件可靠性预测分两个部分进行研究。第一个部分使用了不依赖于软件故障时间记录的静态预测方法。此方法适用于装备软件等现场测试危险性较高,数据难于记录,故障时间难以确定的软件的可靠性预测。在软件可靠性静态预测中,可以近似认为相似软件等量时间内的失效数据可以选用同一种可靠性模型预测,所以可以根据已知的软件测试结果,分析软件服役期故障数历史数据,使用BP神经网络预测同类软件的故障数和可靠度。仿真实验结果与实测数据对比表明BP神经网络静态预测方法是有效可行的。第二个部分是在依据软件故障间隔时间的软件可靠性动态预测中,分析了BP神经网络动态预测方法,并且将其与传统可靠性预测模型进行对比。仿真试验结果表明,BP神经网络软件可靠性预测模型简单易用,并且达到传统可靠性预测模型的估测能力。为了提高预测效率和精度,论文使用基于BP神经网络的聚类方法来选择最佳传统软件可靠性模型进行软件可靠性预测。仿真实验表明,BP神经网络模型选择方法达到目前广泛使用的高斯混合模型选择方法的效果,但其计算相对更简单、方便。
现役舰艇装备软件在通过测试之后,各项技术指标可以达到预定要求,但不一定具有较高的作战效能。即软件技术测试合格的系统仍然不能实际应用。因为软件测试只注重软件本身的故障特性,而忽略了对于装备软件至关重要的软硬件配置拟合度和战术合理性等非技术性指标。论文中结合舰艇装备软件自身的特点,选取特定的可靠性预测参数和特定的可靠性测试结果,收集并整理出舰艇装备软件作战效能数据,建立了舰艇装备软件作战效能评估模型。最后使用并联分级构造方法建立了由静态预测、动态预测和作战效能预测三部分组成的舰艇装备软件可靠性的神经网络预测评估模型。
Warship furnishment software reliability research affect warship's life in battlefield, and can ensure its service time limit. The army weaponry automatic level make progress and development quickly than formerly. The modernistic weaponry is large dense software system. Its software stability and reliability make certain furnishment quality. The technical testing starts barely for warship furnishment at present in the navy testing base China. To comprehensively and accurately on a new generation of warships Wubei appraisal system, I for many years under the sea trial experience in the development of new equipment adapted to the development of ships and equipment software testing and test methods and on the reliability of forecasting tools, vessels and equipment inspection software provide methods and standards.
Papers presented in the software reliability on the basis of analysis of a wide range of commonly used software reliability model, the classic reliability prediction techniques, and a brief introduction of the software reliability engineering. The reliability of traditional forecasting methods are mostly software-based fault of the time, papers will software reliability forecast in two parts for research. The first part is not dependent on the use of a software fault time record of static forecasting methods. This method can be applied to software and other equipment at a higher risk of testing, data difficult to record, it is difficult to determine fault time the reliability of the software forecast. On the software reliability static forecast, similar to software that can approximate equivalent of the time lapse data can be selected with the reliability of a model projections, the software can be the basis of known test results, analysis software failures of service period of historical data, the use of BP Neural networks predicted the failure of similar software and reliability. The simulation results contrast with the measured data show that BP neural network static prediction method is feasible and effective. The second part is based on software in the time between failures of the software reliability dynamic forecasts, BP neural network analysis of the dynamic forecasting methods, and its reliability forecasts with the traditional model of comparison. The simulation results showed that, BP neural network model predicted easy-to-use software reliability, and than most traditional model of a better estimate capacity. In order to improve the efficiency of forecast, the use of paper-based BP neural network clustering method to choose the best model of traditional software reliability software reliability forecasts. The simulation shows that, BP neural network performance for the current widespread use of the Gaussian mixture, but their relative terms more simple, convenient.
Active-duty ships pass the test equipment software, the technical indicators can be achieved scheduled requirements, but not necessarily with high combat effectiveness. Software technology that tested system is still not practical application. Because software testing focused only on the fault of the software itself, and neglect the essential software and equipment for the hardware and software configuration of the fitting and tactical reasonable, and other non-technical indicators. In this paper, in combination with its vessels and equipment characteristics of the software, select the specific parameters of the reliability of specific reliability test, to collect and collate the ship equipment software combat effectiveness data sets, software and equipment to establish the ships operational effectiveness evaluation model. Parallel Application of the final classification structure established a network of BP ships and equipment reliability of the software neural network predictive assessment model framework.
论文在介绍软件可靠性和软件可靠性工程的基础上,分析了多种常用的软件可靠性模型,研究了传统可靠性预测技术。传统的可靠性预测方法大多是基于软件故障时间的,论文将软件可靠性预测分两个部分进行研究。第一个部分使用了不依赖于软件故障时间记录的静态预测方法。此方法适用于装备软件等现场测试危险性较高,数据难于记录,故障时间难以确定的软件的可靠性预测。在软件可靠性静态预测中,可以近似认为相似软件等量时间内的失效数据可以选用同一种可靠性模型预测,所以可以根据已知的软件测试结果,分析软件服役期故障数历史数据,使用BP神经网络预测同类软件的故障数和可靠度。仿真实验结果与实测数据对比表明BP神经网络静态预测方法是有效可行的。第二个部分是在依据软件故障间隔时间的软件可靠性动态预测中,分析了BP神经网络动态预测方法,并且将其与传统可靠性预测模型进行对比。仿真试验结果表明,BP神经网络软件可靠性预测模型简单易用,并且达到传统可靠性预测模型的估测能力。为了提高预测效率和精度,论文使用基于BP神经网络的聚类方法来选择最佳传统软件可靠性模型进行软件可靠性预测。仿真实验表明,BP神经网络模型选择方法达到目前广泛使用的高斯混合模型选择方法的效果,但其计算相对更简单、方便。
现役舰艇装备软件在通过测试之后,各项技术指标可以达到预定要求,但不一定具有较高的作战效能。即软件技术测试合格的系统仍然不能实际应用。因为软件测试只注重软件本身的故障特性,而忽略了对于装备软件至关重要的软硬件配置拟合度和战术合理性等非技术性指标。论文中结合舰艇装备软件自身的特点,选取特定的可靠性预测参数和特定的可靠性测试结果,收集并整理出舰艇装备软件作战效能数据,建立了舰艇装备软件作战效能评估模型。最后使用并联分级构造方法建立了由静态预测、动态预测和作战效能预测三部分组成的舰艇装备软件可靠性的神经网络预测评估模型。
Warship furnishment software reliability research affect warship's life in battlefield, and can ensure its service time limit. The army weaponry automatic level make progress and development quickly than formerly. The modernistic weaponry is large dense software system. Its software stability and reliability make certain furnishment quality. The technical testing starts barely for warship furnishment at present in the navy testing base China. To comprehensively and accurately on a new generation of warships Wubei appraisal system, I for many years under the sea trial experience in the development of new equipment adapted to the development of ships and equipment software testing and test methods and on the reliability of forecasting tools, vessels and equipment inspection software provide methods and standards.
Papers presented in the software reliability on the basis of analysis of a wide range of commonly used software reliability model, the classic reliability prediction techniques, and a brief introduction of the software reliability engineering. The reliability of traditional forecasting methods are mostly software-based fault of the time, papers will software reliability forecast in two parts for research. The first part is not dependent on the use of a software fault time record of static forecasting methods. This method can be applied to software and other equipment at a higher risk of testing, data difficult to record, it is difficult to determine fault time the reliability of the software forecast. On the software reliability static forecast, similar to software that can approximate equivalent of the time lapse data can be selected with the reliability of a model projections, the software can be the basis of known test results, analysis software failures of service period of historical data, the use of BP Neural networks predicted the failure of similar software and reliability. The simulation results contrast with the measured data show that BP neural network static prediction method is feasible and effective. The second part is based on software in the time between failures of the software reliability dynamic forecasts, BP neural network analysis of the dynamic forecasting methods, and its reliability forecasts with the traditional model of comparison. The simulation results showed that, BP neural network model predicted easy-to-use software reliability, and than most traditional model of a better estimate capacity. In order to improve the efficiency of forecast, the use of paper-based BP neural network clustering method to choose the best model of traditional software reliability software reliability forecasts. The simulation shows that, BP neural network performance for the current widespread use of the Gaussian mixture, but their relative terms more simple, convenient.
Active-duty ships pass the test equipment software, the technical indicators can be achieved scheduled requirements, but not necessarily with high combat effectiveness. Software technology that tested system is still not practical application. Because software testing focused only on the fault of the software itself, and neglect the essential software and equipment for the hardware and software configuration of the fitting and tactical reasonable, and other non-technical indicators. In this paper, in combination with its vessels and equipment characteristics of the software, select the specific parameters of the reliability of specific reliability test, to collect and collate the ship equipment software combat effectiveness data sets, software and equipment to establish the ships operational effectiveness evaluation model. Parallel Application of the final classification structure established a network of BP ships and equipment reliability of the software neural network predictive assessment model framework.
引文
[1]崔天意.软件测试和软件开发生命周期.国外科技动态.2004,01
[2]John D Musa.Software Reliability Engineering.McGraw-Hill Companies,Inc,1999:173-223P
[3]Lannino A.Musa J.Software Reliability Theory.Encyclopedia of Software Engineering.Wiley-lnterscience,1994:23-155P
[4]John D.Musa.韩柯译.软件可靠性工程.机械工业出版社,2003,5:16-226页
[5]Hudson G.Program errors as a birth and death process.System Development Corp.Report,1967,SP-30II:234-247P
[6]蔡开元.软件可靠性概要.系统工程与电子技术.1993,04:47-54页
[7]陈光宇.黄锡滋.软件可靠性学科发展现状及展望.电子科技大学学报,2002,4(3):99-102页
[8]刘云.赵纬.软件可靠性研究与进展.微机发展,2003,13(2):12-15页
[9]宋晓秋.软件可靠性评估模型研究.航天科技报告,1997
[10]Jelinski Z.Moranda P.B.Sofrware Reliability Research.Statistical Computer Performance Evaluation.Academic Press,1972:465-484P
[11]Littlewood B.Verrall J.Bayesian A.Reliability Growth Model for omputer Software.Applied Statistics,1973,22(3):332-346P
[12]Nelson E.C.A Statistical Basis for Software Reliability Assessment.TMR-SS-73-03,1973
[13]Schneidewind N.F.Analysis of Error Processes in Computer Software.Sigplan Mote,1975,10(6):.337-346P
[14]Musa J.D.A Theory of Software Reliability and Its Application.IEEE Transactions on Software Engineering,1975,SE-1(3),1975:312-327P
[15]Sukert A.N.ASoftwareReliabilityModelingStudy.RADC-TR-76-247, 1976:34-50P
[16]Goel A.L.K.Okumoto.A Time Dependent Error Detection Rate for a Large Scale Software System.Proceeding of the 3rd USA-Japan Computer Conference,1978:35-40P
[17]Yamanda S.Ohba M.Osaki S.S-Shaped Reliability Growth Modeling for Software Error Detection.IEEE Transaction on Reliability,1983,R-32:475-478P
[18]Yoshihiro Tohma.Kenshin Tokunage.Shinji Nagase.etc.Structural Approach to the Estimation of the Number of Residual Software Faults Based on the Hyper-Geometric Distribution.IEEE Transactions on Software Engineering,March 1989,15(3):345-355P
[19]孙建中.张志华.舰载武器控制系统软件可靠性分配技术研究.船舶工程,2001,06:58-60页
[20]阮镰.陆民燕.韩峰岩.装备软件质量和可靠性管理.国防工业出版社,2006:142-144页
[21]吕励等。软件可靠性模型建立.小型微型计算机系统,1995,12:24-28页
[22]Keiller.P.A.Littlewood.B.Miller.D.R.Safer A.Comparison of Software Reliability Predicions.Proceedings of the 13th IEEE Intermational Symposium on Fault-Tolerant Computing (FTCS-13).Milan.Italy,1983,09:128-134P
[23]Amerit.L.Goel.Software reliability models:Assumptions.limitations and applicability.IEEE transaction on software engineering,1985,SE211,12:1411-1425P
[24]Schick G.J.Wolverton R.W.Assessment of Software Reliability.Proceeding of Oper.Res.Physica Verlag.1973:395-422P
[25]Abdel-GhallyA.A.LittlewoodB.Evaluation of Computing Software Reliability Predictions.IEEE Transactions on Software Engineering,September 1986,SE-12(9):366-377P
[26]Mills H.D.On the Statistical Validation of Computer Program.IBM Federal.Syst.Div.Gaithersburg,MD,Pep.72-6015,1972
[27]Brown J.R.Lipow M.Testing for Software Reliability.Proceeding of International Conference on Reliable Software.Los Angeles.CA,April 1975:518 -527P
[28]Nakagawa Y.Hanata S.An Error Complexity Model for Software Reliabiliy Measurement.Proceeding of International Conference on Software Engineering,1989:230-236P
[29]Michael R.Lyu M.Handbook of Software Reliability Engineering.McGraw -Hill,1996:27-68,119-150,78-108P
[30]Stephen H.Kan.吴明晖等译.软件质量工程-度量与模型(第二版).电子工业出版社,2004:196-202页
[31]邓虹.袁锦虎.邹丰忠.基于变点方法的软件可靠性分析.武汉大学学报(工学版),2005,08:112-115页
[32]Haykin Simon.叶世伟.史忠植译.神经网络原理(第二版).机械工业出版社,2004
[33]周开利.康耀红.神经网络模型及其MATLAB仿真程序设计.清华大学出版社,2005:69-90页
[34]Musa.J.D.Okumoto.K.A.Logarithmic Poisson Execution Time Model for Software Reliability Measurement Proceedings.Seventh International Conference on Seaware Engineering.Orlando.Florida,1984:230-238P
[35]Michacl R.Lyu.刘喜成.钟婉懿译.软件可靠性工程手册.电子工业出版社,1997:19-328页
[36]Musa.J.D.Software Reliability Data.DACS.http://www.dacs.dtic.
[37]汪浩.刘超.金茂忠.基于聚类思想的软件可靠性模型选择.计算机工程与应用,2002,21:144-149P
[38]赵忠华.基于多评价准则的软件可靠性模型选择.新疆大学[硕士论文],2005:20-56页
[39]王莹.崔天意.战术应用软件测试的评估原则.海军装备维修,2003,04
[40]刘庆峰.崔天意.舰艇装备软件靶场测试关键因子分析.海上靶场学术,二○○四年增刊
[2]John D Musa.Software Reliability Engineering.McGraw-Hill Companies,Inc,1999:173-223P
[3]Lannino A.Musa J.Software Reliability Theory.Encyclopedia of Software Engineering.Wiley-lnterscience,1994:23-155P
[4]John D.Musa.韩柯译.软件可靠性工程.机械工业出版社,2003,5:16-226页
[5]Hudson G.Program errors as a birth and death process.System Development Corp.Report,1967,SP-30II:234-247P
[6]蔡开元.软件可靠性概要.系统工程与电子技术.1993,04:47-54页
[7]陈光宇.黄锡滋.软件可靠性学科发展现状及展望.电子科技大学学报,2002,4(3):99-102页
[8]刘云.赵纬.软件可靠性研究与进展.微机发展,2003,13(2):12-15页
[9]宋晓秋.软件可靠性评估模型研究.航天科技报告,1997
[10]Jelinski Z.Moranda P.B.Sofrware Reliability Research.Statistical Computer Performance Evaluation.Academic Press,1972:465-484P
[11]Littlewood B.Verrall J.Bayesian A.Reliability Growth Model for omputer Software.Applied Statistics,1973,22(3):332-346P
[12]Nelson E.C.A Statistical Basis for Software Reliability Assessment.TMR-SS-73-03,1973
[13]Schneidewind N.F.Analysis of Error Processes in Computer Software.Sigplan Mote,1975,10(6):.337-346P
[14]Musa J.D.A Theory of Software Reliability and Its Application.IEEE Transactions on Software Engineering,1975,SE-1(3),1975:312-327P
[15]Sukert A.N.ASoftwareReliabilityModelingStudy.RADC-TR-76-247, 1976:34-50P
[16]Goel A.L.K.Okumoto.A Time Dependent Error Detection Rate for a Large Scale Software System.Proceeding of the 3rd USA-Japan Computer Conference,1978:35-40P
[17]Yamanda S.Ohba M.Osaki S.S-Shaped Reliability Growth Modeling for Software Error Detection.IEEE Transaction on Reliability,1983,R-32:475-478P
[18]Yoshihiro Tohma.Kenshin Tokunage.Shinji Nagase.etc.Structural Approach to the Estimation of the Number of Residual Software Faults Based on the Hyper-Geometric Distribution.IEEE Transactions on Software Engineering,March 1989,15(3):345-355P
[19]孙建中.张志华.舰载武器控制系统软件可靠性分配技术研究.船舶工程,2001,06:58-60页
[20]阮镰.陆民燕.韩峰岩.装备软件质量和可靠性管理.国防工业出版社,2006:142-144页
[21]吕励等。软件可靠性模型建立.小型微型计算机系统,1995,12:24-28页
[22]Keiller.P.A.Littlewood.B.Miller.D.R.Safer A.Comparison of Software Reliability Predicions.Proceedings of the 13th IEEE Intermational Symposium on Fault-Tolerant Computing (FTCS-13).Milan.Italy,1983,09:128-134P
[23]Amerit.L.Goel.Software reliability models:Assumptions.limitations and applicability.IEEE transaction on software engineering,1985,SE211,12:1411-1425P
[24]Schick G.J.Wolverton R.W.Assessment of Software Reliability.Proceeding of Oper.Res.Physica Verlag.1973:395-422P
[25]Abdel-GhallyA.A.LittlewoodB.Evaluation of Computing Software Reliability Predictions.IEEE Transactions on Software Engineering,September 1986,SE-12(9):366-377P
[26]Mills H.D.On the Statistical Validation of Computer Program.IBM Federal.Syst.Div.Gaithersburg,MD,Pep.72-6015,1972
[27]Brown J.R.Lipow M.Testing for Software Reliability.Proceeding of International Conference on Reliable Software.Los Angeles.CA,April 1975:518 -527P
[28]Nakagawa Y.Hanata S.An Error Complexity Model for Software Reliabiliy Measurement.Proceeding of International Conference on Software Engineering,1989:230-236P
[29]Michael R.Lyu M.Handbook of Software Reliability Engineering.McGraw -Hill,1996:27-68,119-150,78-108P
[30]Stephen H.Kan.吴明晖等译.软件质量工程-度量与模型(第二版).电子工业出版社,2004:196-202页
[31]邓虹.袁锦虎.邹丰忠.基于变点方法的软件可靠性分析.武汉大学学报(工学版),2005,08:112-115页
[32]Haykin Simon.叶世伟.史忠植译.神经网络原理(第二版).机械工业出版社,2004
[33]周开利.康耀红.神经网络模型及其MATLAB仿真程序设计.清华大学出版社,2005:69-90页
[34]Musa.J.D.Okumoto.K.A.Logarithmic Poisson Execution Time Model for Software Reliability Measurement Proceedings.Seventh International Conference on Seaware Engineering.Orlando.Florida,1984:230-238P
[35]Michacl R.Lyu.刘喜成.钟婉懿译.软件可靠性工程手册.电子工业出版社,1997:19-328页
[36]Musa.J.D.Software Reliability Data.DACS.http://www.dacs.dtic.
[37]汪浩.刘超.金茂忠.基于聚类思想的软件可靠性模型选择.计算机工程与应用,2002,21:144-149P
[38]赵忠华.基于多评价准则的软件可靠性模型选择.新疆大学[硕士论文],2005:20-56页
[39]王莹.崔天意.战术应用软件测试的评估原则.海军装备维修,2003,04
[40]刘庆峰.崔天意.舰艇装备软件靶场测试关键因子分析.海上靶场学术,二○○四年增刊