Software failure time forecasting method based on kernel partial least squares regression algorithm

A kernel partial least squares, failure time technology, applied in computing, special data processing applications, instruments, etc., can solve problems such as poor model applicability and model prediction accuracy differences, and achieve the effect of improving adaptability and realizing self-adaptive prediction.

Inactive Publication Date: 2013-05-08
HUZHOU TEACHERS COLLEGE
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Problems solved by technology

The stochastic process reliability model is the most researched and widely used category in the field of software reliability growth models, but the statistical components of actual reliability problems cannot be described only by classical statistical distribution functions, and the st

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  • Software failure time forecasting method based on kernel partial least squares regression algorithm
  • Software failure time forecasting method based on kernel partial least squares regression algorithm
  • Software failure time forecasting method based on kernel partial least squares regression algorithm

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Embodiment Construction

[0027] 1) Data normalization

[0028] When using the regression estimation algorithm to learn and predict, you first need to normalize all input and output data to the interval [0.1, 0.9], the specific conversion formula is: Among them, y is the normalized value, x is the actual value, and x max Is the maximum value in the data set, x min Is the minimum value, Δ=x max -x min , After the forecast is over, use the following mapping to map the data back to the actual value: x = y - 0.9 0.8 X Δ + x max .

[0029] 2) Problem conversion

[0030] In the software reliability prediction model based on the kernel function theory, the relationship between the software failure time data and the m failure time data that occurred before it is modeled, and the single-step prediction problem can be transformed into: Known km Observation (T 1 ,t m+1 ),(T 2 ,t m+2 ),L,(T k-m ,t k ) And the k-m+1th input T k-m+1 In the case of estimating the k-m+1th output valu...

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Abstract

The invention discloses a software failure time forecasting method based on a kernel partial least squares regression algorithm. Through the application of a kernel function technology, the problem of software reliability forecast is converted to the problem of recession estimation, and the kernel partial least squares regression algorithm is used for resolving the problem of the software reliability forecast. Through fully consideration of a small sample property of the software reliability forecast, the situations that the size of observational variables is bigger than that of observational samples and multicollinearity exists among the variables can be overcome by using the kernel function technology, and so that a model 'overfitting' situation arises in modeling approaches such as a neural network does not occur. By means of the software failure time forecasting method based on the kernel partial least squares regression algorithm, model parameters are automatically and continuously adjusted to fit the dynamic change in a failure process, therefore adaptive forecasting of the software reliability is achieved, and the adaptive capability of a software failure forecasting model is improved effectively.

Description

【Technical Field】 [0001] The invention relates to a software reliability test and a software failure time data prediction method for the next time or a long time in the future during the evaluation process. 【Background technique】 [0002] Software reliability refers to the probability that the software will not fail within a specified time under specified conditions. The stochastic process reliability model is the most studied and widely used category in the field of software reliability growth models. However, the statistical components of actual reliability problems cannot be described only by classic statistical distribution functions, and the stochastic process model needs to deal with software failures. Many a priori assumptions are made on the attributes of the software and the failure process of the software, which leads to a large difference in prediction accuracy of each model in different projects, that is, the applicability of the model is poor. [0003] The method base...

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Application Information

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IPC IPC(8): G06F19/00
Inventor 蒋云良楼俊钢江建慧申情范婧
Owner HUZHOU TEACHERS COLLEGE
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