| Budget Amount *help |
¥2,000,000 (Direct Cost: ¥2,000,000)
Fiscal Year 1999: ¥900,000 (Direct Cost: ¥900,000)
Fiscal Year 1998: ¥1,100,000 (Direct Cost: ¥1,100,000)
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| Research Abstract |
In order to quantitatively assess software reliability, various software reliability growth models have been proposed. These models describe software failure occurrence and software fault detection in dynamic environments, such as testing in the software development process or in operational use. In most of these models, perfect debugging is assumed, where the fault which caused the software failure is corrected accurately and completely. In practice, however, this is not always true, since many software engineers have the experience that a new fault is introduced through the correction process. In other words, fault correction is an imperfect debugging process. From such a viewpoint, this study has investigated software reliability assessment methods for the imperfect debugging environment, in which the software reliability models proposed in this study are made more practical by considering the uncertainty of fault correction and the possibility of introducing a new fault in the faul
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t correction process. Then, we have developed not only several software reliability growth models under imperfect debugging environment but also software availability models and software safety assessment models. Especially in software reliability growth modeling, it is assumed that there exist two kinds of software failures occurred in the dynamic environment of the software system. One is software failure due to an inherent fault which latently exists before operation, and the other is a software fault which is randomly introduced during operation. The software failure occurrence is described by a nonhomogeneous Poisson process. And in software availability modeling the time-dependent behavior of the software system, which alternates between the operational and restoration state, is described by a semi-Markov process. For software safety assessment modeling, assumed that there exist software faults which can lead the unsafe states in operation, Markovian software safety/reliability models are discussed. Less
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