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Software defect prediction method based on kernel principal component analysis and extreme learning machine

A kernel principal component analysis, software defect prediction technology, applied in software testing/debugging, computer components, error detection/correction, etc., can solve problems such as extreme learning machines that have not been studied and investigated

Inactive Publication Date: 2017-11-14
WUHAN UNIV
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Problems solved by technology

Although extreme learning machines have received high attention in computer vision and pattern recognition, however, no studies have investigated the potential of extreme learning machines for defect prediction

Method used

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  • Software defect prediction method based on kernel principal component analysis and extreme learning machine
  • Software defect prediction method based on kernel principal component analysis and extreme learning machine
  • Software defect prediction method based on kernel principal component analysis and extreme learning machine

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Embodiment Construction

[0033] In order to facilitate those of ordinary skill in the art to understand and implement the present invention, the present invention will be described in further detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the implementation examples described here are only used to illustrate and explain the present invention, and are not intended to limit this invention.

[0034] please see figure 1 , a kind of software defect prediction method based on kernel principal component analysis and extreme learning machine provided by the present invention, comprises the following steps:

[0035] Step 1: Mining the software history warehouse, extracting program modules from it; the granularity of program modules can be set as files, packages, classes or functions, etc. according to the actual application scenarios, and then manually mark the class labels of program modules, Y for defects, and Y for none Defect is N.

[0036] Step ...

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Abstract

The invention discloses a software defect prediction method based on kernel principal component analysis and extreme learning machine. Aiming at the problems that irrelevant module features damage defect prediction model performances in prediction of software defect quantity and the original defect data cannot be accurately represented by the original characteristics, the method disclosed by the invention comprises the following steps: projecting original data into a potential characteristic space through nonlinear mapping by adopting kernel principal component analysis, so that the mapped characteristics are capable of accurately characterizing the complex data structure and increasing the linear separability probability of data in the space; and extracting the representative characteristics of the data, and finally establishing a defect prediction model by adopting an extreme learning mechanism according to the data after characteristic extraction.

Description

technical field [0001] The invention belongs to the technical field of software defect prediction, and relates to a software defect prediction method based on feature selection and integrated learning, in particular to a software defect prediction method based on kernel principal component analysis and extreme learning machine. Background technique [0002] (1) Software defect prediction technology [0003] Software has become an important factor affecting national economy, military affairs, politics and even social life. Highly reliable and complex software systems depend on the reliability of the software they employ. Software defects are the potential source of related system errors, failures, crashes, and even machine crashes. The so-called defect, so far, there are many related terms and definitions in academia and industry, such as failure, defect, bug, error, error, failure, failure, etc. According to ISO 9000, the definition of a defect is: to meet the requirement...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F11/36G06K9/62G06N3/02
CPCG06F11/3604G06N3/02G06F18/2411G06F18/24147G06F18/24
Inventor 伍蔓余啸彭伟强叶思哲刘进
Owner WUHAN UNIV
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