| Budget Amount *help |
¥1,880,000 (Direct Cost: ¥1,700,000、Indirect Cost: ¥180,000)
Fiscal Year 2007: ¥780,000 (Direct Cost: ¥600,000、Indirect Cost: ¥180,000)
Fiscal Year 2006: ¥1,100,000 (Direct Cost: ¥1,100,000)
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| Research Abstract |
We have first discussed the software vulnerability assessment method for the Internet software, which is exposed to the malicious users. After discussing the concept of software vulnerability with expanding the traditional approach of software reliability assessment methods, we have modeled a stochastic model in order to evaluate the software vulnerability as an ex post facto estimation. We have pointed out that classical software reliability measurement methods would not be trustworthy when the software system is operated in the operational environment, which allows anonymity of the users. We have presented the software vulnerability assessment modeling and shown its practical estimation results for the sendmail system.
To develop and improve the software vulnerability assessment methods, we have secondly tackled the problem of making some more microscopic approach for software debugging process. One fruitful result has been obtained by using simulation approach. Especially, our simula
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tion models have an ability of calibration of control parameters included in the simulation models based on a least mean square error rule. This simulation-based approach can treat wide variety of modeling for software reliability/vulnerability assessment.
Also, in order to obtain more applicability for the software reliability/vulnerability models, we have developed a new methodology based on a linearized growth curve model. In the literature of software reliability/vulnerability assessment, there have been many assessment models have been proposed so far. They are the achievement of studies performed by a lot of researchers. Various approaches have been tried to describe software reliability quantitatively through the observation of software development processes. Namely, there are the models which use stochastic processes, non-parametric models, neural networks, and so on. This fact shows that each model has some advantages for several data sets that are analyzed in the paper itself ; however, the model is not always applicable to all kinds of data. Therefore, we have a number of software reliability models. In order to overcome this complication of model selection, we have discussed a method of generalizing several proposed software reliability data analysis. These new methods can open a new door of software vulnerability assessment methods. Less
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