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Warship Furnishment Software Reliability Testing Method and Forecast

Author: CuiTianYi
Tutor: ZhangLiJun
School: Harbin Engineering University
Course: Control Theory and Control Engineering
Keywords: software reliability warship furnishment software neural networks Clustering Method software reliability forecast
CLC: TP311.52
Type: Master's thesis
Year: 2008
Downloads: 204
Quote: 1
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Abstract


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.

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CLC: > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > Computer software > Program design,software engineering > Software Engineering > Software Development
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