Automatic checking method for change influence degree of model

Abstract

The invention discloses an automatic checking method for change influence degree of a model. The automatic checking method comprises the following steps: a), collecting and analyzing SQL (structured query language) sentences performed by a database, and recording correlation between tables in the database; b) generating a correlation base line of a system data model to obtain a constrained graph of a system physical model, generating a physical model digraph according to the system service flow, and constructing a data model-service functional diagraph; c) when the software system has model changes, inputting the corresponding change information, and distinguishing the change influence range and the service functions according to the digraph and the functional digraph. According to the automatic checking method for change influence degree of the model disclosed by the invention, the SQL information of the database is collected, the model digraph is generated, the software change influence degree is analyzed from the software system physical model, the change influence range and the service function pots of the model are intelligently judged, and the risk and design defects are distinguished in advance, so that the intuition, the sufficiency and the efficiency of the change influence degree analysis are improved.

Description

technical field [0001] The invention relates to a data model processing method, in particular to an automatic checking method for model change influence degree. Background technique [0002] The iterative update of the software system is inevitable and very frequent in the software life cycle. When the software is changed, the source code and physical model of the system software will be changed accordingly. Every version change, especially for the transaction business of large enterprises The system is mainly based on relational databases, which are accompanied by a large number of physical model changes (tables, field additions, deletions, index changes, etc.), and specific business logic is realized by adding, deleting, modifying and querying related table data in relational databases. The above operations will inevitably affect the stability of the original system. At present, there are various methods for judging the impact of software changes, such as function diagram ...

AI Technical Summary

Problems solved by technology

Lack of inspection of the impact of physical model changes, unable to provide support for system development and operation and maintenance, and unable to judge the impact of model changes on other system services or data; the analysis of the impact of physical models on business is also impossible, and it is impossible to analyze the impact of affected users before and after going online. Targeted testing of affected businesses

[0004] At present, the software systems of large enterprises have a large amount of data, complex business logic, and complex physical models of the system. Basically, no one can fully grasp these database tables, and the requirements change frequently, and the development time is short, which brings great impact on software change design and operation and maintenance. big challenge

The existing technology has the following disadvantages: 1. Lack of evaluation on the change dimension of the physical model of the system, and the omissions are not comprehensive enough; 2. Failures cannot be prevented, and problems can only be found in the online test stage of requirements. Harsh, there is a lag; 3. System developers and testers are required to have a high degree of familiarity with the system, which is closely dependent on people's experience and technical level and other conditions, and there is a certain degree of passivity and blindness

[0005] It can be seen from the above that the existing technical solutions are all post-processing, which requires a lot of manpower and time to process. Developers cannot judge the degree of influence during the design stage, and operation and maintenance personnel cannot understand the attributes and uses of the database model when a fault occurs. Perform fault location

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