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1. CN109522192 - A forecasting method based on knowledge map and complex network combination

Office
China
Application Number 201811209128.3
Application Date 17.10.2018
Publication Number 109522192
Publication Date 26.03.2019
Grant Number 109522192
Grant Date 04.08.2020
Publication Kind B
IPC
G06F 11/34
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
FELECTRIC DIGITAL DATA PROCESSING
11Error detection; Error correction; Monitoring
30Monitoring
34Recording or statistical evaluation of computer activity, e.g. of down time, of input/output operation
CPC
G06F 11/3452
GPHYSICS
06COMPUTING; CALCULATING; COUNTING
FELECTRIC DIGITAL DATA PROCESSING
11Error detection; Error correction; Monitoring
30Monitoring
34Recording or statistical evaluation of computer activity, e.g. of down time, of input/output operation ; ; Recording or statistical evaluation of user activity, e.g. usability assessment
3452Performance evaluation by statistical analysis
Applicants BEIHANG UNIVERSITY
北京航空航天大学
Inventors YANG SHUNKUN
杨顺昆
GOU XIAODONG
苟晓冬
LI HONGMAN
李红曼
HUANG TINGTING
黄婷婷
LIN OUYA
林欧雅
LI DAQING
李大庆
TAO FEI
陶飞
SHE ZHIKUN
佘志坤
Agents 北京慧泉知识产权代理有限公司 11232
北京慧泉知识产权代理有限公司 11232
Title
(EN) A forecasting method based on knowledge map and complex network combination
(ZH) 一种基于知识图谱和复杂网络组合的预测方法
Abstract
(EN)
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.

(ZH)
本发明提供一种基于知识图谱和复杂网络组合的预测方法,步骤包括:获取多个不同类型的软件故障案例;故障案例中的多个故障现象以及故障原因并进行聚类分析;提取现象聚类及原因聚类的关键词作为每一类的聚类标签,生成知识图谱;将聚类标签分别对应到软件的多个功能模块;获取各个功能模块与所述软件代码之间的映射关系;建立代码网络;将聚类标签所对应到的功能模块映射到各个版本下的代码网络中,在相应的代码部分作出标记,预测未知版本软件的代码网络风险的位置所在。本发明能实现有效的将具体的软件故障对应标识到代码网络中,进而对未知版本的软件代码网络进行风险预测,进而实施有效的规避风险的措施。