Bug knowledge modeling method based on graphic database

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...

AI Technical Summary

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-structured data changes and queries, making the search bogged down in the quagmire of hyperlinked tables and reducing search efficiency; secondly , as the size of the data set continues to increase, the reliability of the association between the data will continue to decrease, which will lead to performance problems, and it is also very inconvenient to design and use. Once the data model changes, the corresponding maintenance costs will increase. very expensive

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