Bug knowledge modeling method based on graphic database

A knowledge modeling and database technology, applied in the field of software debugging, can solve problems such as not in-depth, unfavorable for developers to solve problems, and reduce search efficiency.

Active Publication Date: 2016-05-25
YANGZHOU UNIV
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Problems solved by technology

[0003] Before the present invention was made, so far, there was no bug search engine combined with knowledge graphs, and most of the searches were too superficial, and only the first-level related content was returned, which was not in-depth, which was not conducive to developers to solve problems
Existing searches supported by relational databases will lead to a large number of table connections when dealing with a large number of complex, interlinked, and low-s

Method used

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  • Bug knowledge modeling method based on graphic database
  • Bug knowledge modeling method based on graphic database
  • Bug knowledge modeling method based on graphic database

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Embodiment Construction

[0025] Technical thinking of the present invention is:

[0026] The method of the invention is to optimize the database of bug knowledge and help developers better understand bugs and deepen their understanding of software faster. The term extraction is performed on the short text content of the bug information, and the relatively important words in the document are selected using the TF-lDF standard. Synthetically extracted terms, keywords provided by bugzilla and artificially established dictionaries construct feature vectors, and use the KNN algorithm based on K-Medoids optimization to analyze the brief information (bugMSG), description information (description), and comment information (comment) Classify each kind of text separately, so as to realize the classification of corresponding bugs.

[0027] A search system based on knowledge graphs can bring users a new search experience and provide users with knowledge rather than just information. This requires a graph databas...

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Abstract

The invention relates to a bug knowledge modeling method based on a graphic database. According to the method, term extraction is performed on short text content in bug information, and relatively important vocabularies in a document are selected by the adoption of the TF-IDF standard; feature vectors are established for comprehensively extracted terms, keywords provided by bugzilla and a dictionary built artificially, a KNN algorithm based on K-Medoids optimization is adopted for classifying sketch information (bug MSG) texts, description information (description) texts and comment information (comment) texts respectively, and therefore classification of corresponding bugs is realized. The method overcomes the defects of complexity, interlinkage, lower-structuralization data change and a large amount of table connection by means of an existing search method. According to the method, starting from the graphic database, a database of bug information is optimized, it is not limited to solution to a single problem, multi-angle correlation can be provided, and the graphic database is a high-performance data structural form used for storing data.

Description

technical field [0001] The invention belongs to the field of software debugging, in particular to a bug knowledge modeling method based on a graphic database. Background technique [0002] The maintenance of software bugs runs through the software life cycle. When a software practitioner encounters a difficult software bug, an efficient bug search engine with multi-dimensional knowledge is very important to help him better understand and solve the encountered bug. When he searches, the search engine can not only return bugs related to the search content, but also return other bugs related to the bug he searches for. [0003] Before the present invention was made, so far, there was no bug search engine combined with knowledge graphs, and most of the searches were too superficial, returning only first-level related content, which was not in-depth, which was not conducive to developers to solve problems. Existing searches supported by relational databases will lead to a large...

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Application Information

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IPC IPC(8): G06F17/30
CPCG06F16/367G06F16/374G06F16/51
Inventor 孙小兵徐淑华李斌王璐
Owner YANGZHOU UNIV
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