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Design method of software defect prediction model based on kernel principal component analysis algorithm

A technology for software defect prediction and nuclear principal component analysis, applied in software testing/debugging, computer components, computing, etc., can solve problems such as measuring metadata redundant data, and achieve the effect of solving data redundancy and avoiding loss

Inactive Publication Date: 2019-01-08
BEIJING INSTITUTE OF TECHNOLOGYGY
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Problems solved by technology

[0009] In view of this, the present invention provides a software defect prediction model design method based on the kernel principal component analysis algorithm, which can solve the problem of redundant data in the measurement element of software defects and improve the accuracy of machine learning algorithms

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  • Design method of software defect prediction model based on kernel principal component analysis algorithm
  • Design method of software defect prediction model based on kernel principal component analysis algorithm
  • Design method of software defect prediction model based on kernel principal component analysis algorithm

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[0023] The present invention will be described in detail below with reference to the accompanying drawings and examples.

[0024] Such as figure 1 As shown, the present invention provides a method for designing a software defect prediction model based on the core principal component analysis algorithm, comprising the following steps:

[0025] Step 1. Determine the data set used to train the model, organize the data set so that the samples in the data set are suitable for model training and testing, and divide the data set into a training set and a test set;

[0026] Step 2. Use the kernel principal component analysis algorithm to reduce the dimensionality of the training set: select the kernel function and determine the parameters of the kernel function, select the dimensionality of the dimensionality reduction, and then reduce the dimensionality of the training set;

[0027] Step 3. Use the reduced-dimensional training set as input, select the Gaussian (RBF) function as the ...

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Abstract

The invention discloses a design method of a software defect prediction model based on a kernel principal component analysis algorithm. The method includes: step 1, determining a data set for traininga model, and dividing the data set into a training set and a test set; step 2, carrying out dimensionality reduction treatment on that training set by using a kernel principal component analysis algorithm: selecting kernel functions and determining parameter of the kernel functions, selecting dimensionality reduction dimensions, and then reducing the dimensionality of the training set; step 3, taking the reduced-dimension training set as input, selecting Gauss (RBF) function as SVM kernel function, defining value interval and step size, and using grid search to find the optimal solution of SVM parameter penalty factor C and kernel function parameter sigma through cross-validation experiment method of ten fold; step 4, testing the performance of the model through a test set, and completingthe design of the software defect prediction model. The invention can solve the problem of redundant data in the measurement element of the software defect, and improve the accuracy rate of the machine learning algorithm.

Description

technical field [0001] The invention belongs to the field of software safety, and in particular relates to a method for designing a software defect prediction model based on a kernel principal component analysis algorithm. Background technique [0002] Software defect: from the internal point of view of the product, a defect is an error or problem in the process of software product development or maintenance; from the external point of view of the product, a defect is the failure or violation of a certain function that the system needs to implement. Therefore, a software defect is actually a software product that does not meet the expected functions and does not meet the specification requirements, causing inconvenience in use. [0003] Principal Component Analysis (PCA): Principal Component Analysis is a non-parametric dimensionality reduction algorithm that is easy to use and can effectively solve problems such as data redundancy and data noise, so it is widely used in the...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F11/36G06K9/62
CPCG06F11/366G06F18/2411G06F18/214
Inventor 单纯张思聪孙世有危胜军刘臻
Owner BEIJING INSTITUTE OF TECHNOLOGYGY
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