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Software reliability model parameter estimation method based on mixed wolf pack algorithm

A technology of model parameters and wolf pack algorithm, applied in the field of software reliability model evaluation, can solve the problems of easy to fall into local optimization and low accuracy of solution, and achieve the effect of optimal parameter estimation results, stable estimation results, and high stability

Inactive Publication Date: 2020-02-28
JIANGSU UNIV OF SCI & TECH
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Problems solved by technology

The advantage of the PSO algorithm is that there are few parameters to set, it is easier to implement, and the convergence speed in the early stage is faster, but it is easy to fall into the local optimization during the search process, resulting in low accuracy of the solution.

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  • Software reliability model parameter estimation method based on mixed wolf pack algorithm
  • Software reliability model parameter estimation method based on mixed wolf pack algorithm
  • Software reliability model parameter estimation method based on mixed wolf pack algorithm

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Embodiment Construction

[0047] In order to deepen the understanding of the present invention, the present invention will be described in further detail below in conjunction with the accompanying drawings and embodiments, which are only used to explain the present invention and do not limit the protection scope of the present invention.

[0048] Such as figure 1 Shown is a schematic diagram of the operation flow chart of the present invention.

[0049] In the embodiment of the present invention, the selected experimental data comes from five groups of software failure interval data sets SYS1, SS3, CSR1, CSR2, and CSR3 obtained in actual industrial projects, and the data download address is http: / / www.cse.cuhk. edu.hk / lyu / book / reliability / data.html. The actual failure numbers of SYS1, SS3, CSR1, CSR2, and CSR3 are 136, 278, 397, 129, and 104, respectively.

[0050] (1) Use all the data of the five data sets of SYS1, SS3, CSR1, CSR2, and CSR3. Select the software reliability model, taking the G-O model...

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Abstract

The invention relates to the technical field of software reliability model evaluation, and particularly relates to a software reliability model parameter evaluation method based on a mixed wolf pack algorithm. The method comprises: firstly, selecting a software reliability model, initializing all parameters of a particle swarm, then constructing a fitness function through a maximum likelihood estimation formula of the parameters of the software reliability model, then mixing the wolf pack algorithm and the particle swarm algorithm, and finally obtaining the optimal parameter value of the software reliability model based on the mixed algorithm. The method is characterized in that the particle swarm algorithm is used for further exerting the advantages of the wolf pack algorithm, and a parameter estimation result better than that of a single algorithm is obtained. Moreover, the stability of the result can be improved, and the estimation result is more stable than that of a single algorithm. Conditions are such even when the amount of data is reduced or the number of algorithm iterations is changed.

Description

technical field [0001] The invention relates to the technical field of software reliability model evaluation, in particular to a software reliability model parameter evaluation method based on a mixed wolf pack algorithm. Background technique [0002] Software reliability is a qualitative index to measure software quality and has important research significance, so it has been paid more and more attention by researchers. So far, researchers have published nearly a hundred software reliability models, such as G-O model, M-O model and J-M model. However, these models are nonlinear function models, and it is difficult to directly estimate their parameters, so a new idea is to apply intelligent optimization algorithms to model parameter estimation. [0003] Wolf Pack Algorithm (WPA) is a typical swarm intelligence algorithm, which has similar group behaviors to other group creatures, but wolves have a stricter mechanism for predation and the distribution of prey. Collaboration...

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Application Information

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IPC IPC(8): G06F11/36G06N3/00
CPCG06F11/3608G06N3/006
Inventor 李震杨柳詹梦园孙晨旭蒋征骐
Owner JIANGSU UNIV OF SCI & TECH
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