Software System State Assessment Method Based on Parameter Correlation
A software system and state evaluation technology, applied in the field of computer software, can solve problems such as uncertainty, lack of mature methods, and software system complexity, and achieve the effects of reducing errors, facilitating accurate evaluation, and improving accuracy
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Embodiment 1
[0040] Such as figure 1 As shown, the software system state assessment method based on parameter correlation includes the following steps:
[0041] Step 1. Determine the indicators and status parameters at all levels of the software system. In this embodiment, the software system using the oracle server and the WebLogic server is used as an example. The status parameters of the software system operation are 45 in total, and the indicators and status parameters at all levels are set. As shown in Table 1.
[0042] Table 1 State parameters of software system operation
[0043]
[0044]
[0045] Step 2, determine the state parameter type, wherein, the state parameter that the software system performance increases with the state parameter value increases is the positively correlated parameter, the state parameter that the software system performance decreases with the state parameter value increases is the negatively correlated parameter; In this step, The state parameters ...
Embodiment 2
[0074] On the basis of Embodiment 1, the software system state assessment method based on parameter correlation in this embodiment also includes the steps of obtaining the normal state data of each state parameter and determining the normal value of the state parameter through the K-means clustering algorithm, This step is between step 1 and step 5, and specifically includes the following steps:
[0075] (a) Obtain the state data sample set X of the software system, which is the training set. Assume that the size of the training set is m, and the training set is {x (1) , x (2) , x (3) ,...,x (m)}, each sample includes the values of all state parameters, since a total of 45 state parameters are collected in this embodiment, the dimension of each training sample is 45, namely x (p) ∈ R 45 , p=1,2,...,m; R 45 Represents a 45-dimensional real number space.
[0076] (b) Using the K-means clustering algorithm to cluster all samples in the sample set into k categories, specif...
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