Automatic generation method and device for testing data of database and testing system

Abstract

The invention provides an automatic generation method and device for testing data of a database and a testing system. The method comprises the steps that a database list needing to generate testing data is selected; parameters of the database list are inquired; according to the parameters of the database list, generation rules of the testing data are selected, and the testing data are generated; insert rules of the testing data are selected, and the generated testing data are inserted into the database list according to the insert rules. According to the automatic generation method and device for testing data of the database and the testing system, the technical problem of field correlation foreign key recognition is solved, various flexible testing data generation rules are provided, the intelligent insert rules are provided, the data insert speed is greatly increased, a user interface is provided, and operation is easy.

Description

technical field [0001] The invention relates to an automatic generation method, device and test system of test data, in particular to an automatic generation method, device and test system for automatically generating test data in a database. Background technique [0002] One of the most important tasks in software testing is the preparation and maintenance of test data. Making test data is an important and necessary link before software testing is performed, and this work is cumbersome, time-consuming, repetitive and extremely error-prone, so finding A common, visual approach to generating test data is very meaningful and important. Especially in the field of database automation testing and performance testing, a large amount of test data is required for testing. The demand for test data will be hundreds of thousands, millions or even tens of millions of levels, and the required data is generally not solidified single data, but It is the data with certain rules, and there ...

AI Technical Summary

Problems solved by technology

[0002] One of the most important tasks in software testing is the preparation and maintenance of test data. Making test data is an important and necessary link before software testing is performed, and this work is cumbersome, time-consuming, repetitive and extremely error-prone, so finding A general, visual approach to generating test data is very meaningful and important

Especially in the field of database automation testing and performance testing, a large amount of test data is required for testing. The demand for test data will be hundreds of thousands, millions or even tens of millions of levels, and the required data is generally not solidified single data, but It is the data with certain rules, and there are various relationships between the data, so the preparation of the test data requires a lot of manpower and time

Moreover, the larger the amount of test data generated and inserted, the more complex the test data rules will be, and the more manpower will be consumed. The existing test tool data generation rules are single, and basically rely on manual generation and maintenance. The generated data Poor reliability and poor integration between data have brought great troubles to the testing work

[0003] Especially when the existing database test data is generated, it is impossible to judge whether the fields in the database table are associated with foreign keys, resulting in inaccurate generated test data, and it is impossible to check whether the associated data between the database tables is correct

[0004] In the prior art, the generated test data is inserted into the database through the batch processing operation of java. Since the batch processing of java is inserted into the database, efficiency issues will be involved, and the number, size, and type of fields are different, which will directly affect the insertion efficiency. Different batch insertion volumes will also affect insertion efficiency

[0005] Existing database tests cannot be operated visually, with low operational efficiency and low accuracy

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