Software Failure Time Prediction Method Based on Correlation Vector Regression Estimation

A regression estimation and failure time technology, applied in the field of software failure time data prediction, which can solve problems such as over-learning applicability.

Active Publication Date: 2016-06-15
HUZHOU TEACHERS COLLEGE
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Problems solved by technology

When solving the problem of reliability prediction, the traditional solution reflects the philosophy

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  • Software Failure Time Prediction Method Based on Correlation Vector Regression Estimation
  • Software Failure Time Prediction Method Based on Correlation Vector Regression Estimation
  • Software Failure Time Prediction Method Based on Correlation Vector Regression Estimation

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Embodiment Construction

[0036] 1) Data normalization

[0037] When using the regression estimation algorithm for learning prediction, it is first necessary to normalize all input and output data to the interval [0.1,0.9]. The specific conversion formula is: y = 0.8 Δ x + ( 0.9 - 0.8 × x m a x Δ ) , 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, the following mapping is used to map the data back to the actual value:

[0038] 2) Problem Transformation

[0039] Assume that the software failure time that has occurred is t 1 ,t 2 ,...,t n , let t l =...

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Abstract

The invention discloses a software failure time forecasting method based on relevant vector quantity regression estimation. Software failure time and time data with the number of m before the software failure time are learnt to accordingly capture interior dependency relations between failure time, and therefore the forecasting method based on reliability of relevant vector machine software is built. Because small sampling characteristics of forecasting of reliability of the software are fully considered, a kernel function technology can overcome the situations that the number of observational variables is larger than the number of observation samples and multi-collinearity exists between variables, the condition that model overfitting generated by modeling methods such as neural networks can be avoided. In the new forecasting method, model parameters are automatically adjusted to adapt to dynamic changing of the failure process, and therefore self-adaptation forecasting of reliability of the software is achieved and adaptive capacity of a forecasting model of software failure is effectively improved.

Description

【Technical field】 [0001] The invention relates to a software reliability test and a method for predicting software failure time data in the next or a long time in the future. 【Background technique】 [0002] Software reliability refers to the probability that software will not fail within a specified time under specified conditions. When solving the problem of reliability prediction, traditional solutions reflect the philosophy of large-sample statistics, and are prone to problems such as over-learning and poor applicability. [0003] Statistical learning theory is based on a relatively solid theoretical foundation, which provides a unified framework for solving finite sample learning problems. It can incorporate many existing methods, and is expected to help solve many problems that were difficult to solve, such as neural network structure selection problems, local minimum point problems, etc. Relevance vector machine (relevancevectormachine, RVM) is a sparse Bayesian lear...

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Application Information

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IPC IPC(8): G06F11/36
Inventor 蒋云良楼俊钢沈张果范婧
Owner HUZHOU TEACHERS COLLEGE
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