| Summary: | 博士 === 國立成功大學 === 工業與資訊管理學系碩博士班 === 97 === For the last two decades, reliability growth has been studied to predict software reliability in the testing/debugging phase. Most of the models developed were based on the Non-Homogeneous Poisson Process (NHPP), and S-shaped type or exponential-shaped type of behavior is usually assumed. Unfortunately, such models may be suitable only for particular software failure data, thus narrowing the scope of applications. Therefore, from the perspective of learning effects that can influence the process of software reliability growth, this study considered that efficiency in testing/debugging concerned not only the ability of the testing staff, but also the learning effect that comes from inspecting the testing/debugging codes. The proposed model can reasonably describe the S-shaped and exponential-shaped types of behaviors simultaneously, and the results in the experiment show good fit. Moreover, a Bayesian approach was also employed in the study under the cases with insufficient historical data and with different testing environments. Besides, an optimal software release policy is suggested, and the numerical examples are given to verify the effectiveness of the proposed approach, and the sensitive analyses are performed in light of the numerical examples.
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