A forecasting method based on knowledge map and complex network combination

A knowledge map and complex network technology, applied in the field of software prediction, can solve problems such as inability to improve the code layer, lack of code program modules, and inability to effectively avoid code network risks.

Active Publication Date: 2019-03-26
BEIHANG UNIV
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Problems solved by technology

[0003] However, the model established by this method can usually only predict the number of software faults and the rough location of the code program corresponding to the fault. For more detailed fault categories, it is not reflected in the corresponding code program module.
When a failure occurs, relevant technical personnel cannot accurately and adequately improve the code layer, so that they cannot effectively avoid code network risks

Method used

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  • A forecasting method based on knowledge map and complex network combination
  • A forecasting method based on knowledge map and complex network combination
  • A forecasting method based on knowledge map and complex network combination

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Embodiment Construction

[0029] The following will combine the attached Figure 1-3 Embodiments of the present invention will be described in detail.

[0030] The present invention is a prediction method based on the combination of knowledge graph and complex network, such as figure 1 As shown, its implementation steps are as follows:

[0031] 101. Acquire multiple different types of software failure cases.

[0032] Wherein, the types of software failure cases include at least: fault tolerance and fault prevention, interface, interrupt and site protection, timing and time limit, operating environment, calculation and algorithm, initialization and reset, programming and language usage, requirement management and configuration management. The software failure case is as follows: key instructions are not defined as redundant bits, resulting in functional failure. The causes, phenomena, and impact on the software system of each defect case collected, even including the time when the defect occurred, ar...

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Abstract

The invention provides a prediction method based on a knowledge map and a complex network combination. The method comprises the following steps: acquiring a plurality of software fault cases of different types; anyalzing Multiple fault phenomena and fault causes in fault cases by clustering analysis. The key words of phenomenon clustering and cause clustering are extracted as clustering labels ofeach category to generate knowledge map. Wherein Clustering labels are respectively corresponding to a plurality of functional modules of the software; Acquiring a mapping relationship between each functional module and the software code; Setting up code network; mapping The functional modules corresponding to the clustering label to the code network under each version, and the corresponding codepart is marked to predict the location of the code network risk of the unknown version software. The invention can effectively mark the specific software fault correspondence into the code network, and then predict the risk of the unknown version of the software code network, and then implement effective risk avoidance measures.

Description

technical field [0001] The invention provides a prediction method based on the combination of knowledge graph and complex network, which belongs to the field of software prediction. Background technique [0002] With the development of science and technology, software is used more and more frequently, which puts forward higher requirements for the accuracy and efficiency of software prediction. Usually, people’s prediction method for software failure is to filter out the measurement elements related to software failure by analyzing software code or development process, and then create a failure prediction data set by mining software history warehouse, and build a failure prediction model to predict The number of potential faults of the software under test and the approximate program location where the fault occurred. [0003] However, the model established by this method can usually only predict the number of software faults and the rough location of the code program corres...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F11/34
CPCG06F11/3452
Inventor 杨顺昆苟晓冬李红曼黄婷婷林欧雅李大庆陶飞佘志坤
Owner BEIHANG UNIV
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