Non-parametric Estimation for NHPP Software Reliability Models |
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Authors: | Zhiguo Wang Jinde Wang Xue Liang |
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Affiliation: | a Department of Mathematics and Key Lab for Novel Software Technology, Nanjing University, People's Republic of Chinab Department of Mathematics, University of Science and Technology of Suzhou, People's Republic of China |
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Abstract: | The non-homogeneous Poisson process (NHPP) model is a very important class of software reliability models and is widely used in software reliability engineering. NHPPs are characterized by their intensity functions. In the literature it is usually assumed that the functional forms of the intensity functions are known and only some parameters in intensity functions are unknown. The parametric statistical methods can then be applied to estimate or to test the unknown reliability models. However, in realistic situations it is often the case that the functional form of the failure intensity is not very well known or is completely unknown. In this case we have to use functional (non-parametric) estimation methods. The non-parametric techniques do not require any preliminary assumption on the software models and then can reduce the parameter modeling bias. The existing non-parametric methods in the statistical methods are usually not applicable to software reliability data. In this paper we construct some non-parametric methods to estimate the failure intensity function of the NHPP model, taking the particularities of the software failure data into consideration. |
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Keywords: | Software reliability NHPP model intensity function non-parametric estimation |
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