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Software defect positioning method and processing device based on manifold regularization width learning

A software defect and positioning method technology, applied in machine learning, electrical digital data processing, software testing/debugging, etc., can solve problems such as poor positioning effect and low efficiency

Pending Publication Date: 2022-02-01
HENAN UNIVERSITY OF TECHNOLOGY
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Problems solved by technology

[0006] The purpose of the present invention is to provide a software defect location method and processing device to solve the current problem of poor defect location and low efficiency

Method used

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  • Software defect positioning method and processing device based on manifold regularization width learning
  • Software defect positioning method and processing device based on manifold regularization width learning
  • Software defect positioning method and processing device based on manifold regularization width learning

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Embodiment Construction

[0041] The present invention will be described in further detail below in conjunction with the accompanying drawings.

[0042] The present invention provides a software defect localization processing device based on manifold regularization width learning, including a memory, a processor, and a computer program stored in the memory and operable on the processor, wherein the processor executes a manifold regularization The software defect location of breadth learning, through static instrumentation, at the same time, it plans to use the forward calculation method to calculate the program's backward dynamic slice, so as to obtain the program spectrum and test case execution results; find the optimal parameters according to the grid search method, and use the width learning Introduce the concept of local scatter on the basis of , use the idea of ​​locally maintaining projection to construct local scatter, and introduce local class scatter into the width learning model, which can be...

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Abstract

The invention provides a software defect positioning method and processing device based on manifold regularization width learning, and belongs to the technical field of software test. The method comprises the steps: firstly, executing a to-be-tested program and a test case through instrumentation, and performing static analysis at the same time to obtain a program spectrum and a test case execution result; introducing a local divergence concept into software defect positioning and manifold regularization width learning, and constructing local divergence by applying a local preserving projection thought; introducing the local class divergence into a width learning model, so the classification effect of manifold regularization width learning in software defect positioning is improved; finally, sequencing the program statements according to the occurrence frequency of the program statements in the failed simulation test case coverage vector from more to at least as a suspicion ranking list, and guiding defect positioning. Through manifold regularization width learning, data can keep an original geometric structure in a new feature space, and the method has the characteristics of simple structure, high efficiency and the like.

Description

technical field [0001] The invention relates to the technical field of software testing, in particular to a software defect location method and processing device based on manifold regularization width learning. Background technique [0002] With the rapid development of computer technology, computer software has been widely used in all aspects of society. At the same time, with the rapid growth of computer usage, it has become more and more difficult to ensure its quality and maintenance tasks. Software quality and maintenance issues have become one of the focuses of widespread concern in society. [0003] Software defect location technology is an analysis method to determine the specific location of the defect when some test cases fail to execute after executing the test case set. Machine learning techniques are adaptive and robust, and can generate models based on data with limited human-computer interaction. In the context of defect localization, the problem at hand can...

Claims

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Application Information

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IPC IPC(8): G06F11/36G06K9/62G06N20/00
CPCG06F11/3684G06F11/3688G06F11/3676G06N20/00G06F18/24
Inventor 曹鹤玲宋昌隆何正浩邓淼磊李磊楚永贺廖天力张硕
Owner HENAN UNIVERSITY OF TECHNOLOGY
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