Software defect prediction method and system

A software defect prediction and algorithm technology, applied in the field of software security, can solve problems such as low prediction accuracy

Active Publication Date: 2014-05-21
BEIJING INSTITUTE OF TECHNOLOGYGY
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[0004] The present invention provides a software defect prediction method and a software defect prediction syst

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  • Software defect prediction method and system
  • Software defect prediction method and system

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[0040] In order to make the object, technical solution and advantages of the present invention clearer, the implementation manner of the present invention will be further described in detail below in conjunction with the accompanying drawings.

[0041] The technical idea of ​​the present invention is aimed at the limitations of the existing dimensionality reduction methods, that is, the result after dimensionality reduction cannot guarantee the integrity of the data, nor is it the best embodiment of the intrinsic dimensionality. The embodiment of the present invention adopts the locally linear embedding (LLE for short) algorithm to reduce the dimensionality of the software defect data set. The idea of ​​the algorithm is to start from the spatial structure of the sample data and to ensure the geometric structure of the data sample after dimensionality reduction. Unchanged, so that the data after dimensionality reduction can more completely reflect the various characteristics of ...

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Abstract

The invention provides a software defect prediction method and system. The software defect prediction method and system are used for resolving the problem that existing software defect prediction is low in accuracy. The software defect prediction system comprises a dimension reduction processing unit, an SVM training unit and a defect prediction unit. The software defect prediction method comprises the steps that firstly, dimension reduction processing is conducted on a first training dataset according to the LLE, a lower-dimension vector, mapped into a low-dimension space, of each sample point in the first training dataset is obtained, and a second training dataset composed of the lower-dimension vectors is obtained; secondly, training is conducted on an SVM classifier according to the second training dataset, an optimal classification hyperplane function of the SVM classifier is obtained, and the trained SVM classifier is further obtained; thirdly, detect prediction is conducted on software to be predicted according to the trained SVM classifier.

Description

technical field [0001] The invention relates to the field of software security, in particular to a software defect prediction method and a software defect prediction system. Background technique [0002] Software defect prediction technology was born in the 1970s, and its main function is to guide quality assurance work and provide high-value reference for balancing software costs. Software defect prediction is mainly divided into dynamic prediction and static prediction. At present, main researches are concentrated on static prediction. The present invention belongs to distribution prediction technology in static prediction. Support Vector Machine (SVM for short) is a new machine learning method developed on the basis of statistical learning theory. It has many unique advantages in solving small sample, nonlinear and high-dimensional pattern recognition. The existing Software defect prediction mainly utilizes the support vector machine (SVM) tool to establish a prediction ...

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Application Information

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IPC IPC(8): G06F11/36
Inventor 胡昌振单纯陈博洋马锐王勇
Owner BEIJING INSTITUTE OF TECHNOLOGYGY
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