Software testing data generation method and device

Abstract

The invention discloses a software testing data generation method and device. The method comprises the steps that an interface document of each interface of a server is automatically analyzed, interface request data of a standard structure is generated and saved; each piece of the interface request data is parameterized; service processes including multiple associated interfaces are packaged into independent data generation rules, and each data generation rule is defined through a label; the data generation rule corresponding to the label is called through the label and is used for generating the needed service data. By means of the method, operation can be visualized, and a data sample can be controlled; in a software system with complicated service logic, deep path and high coupling, it is unnecessary for testing personnel to learn the complicated prepositional service logic of a current tested object, it is only necessary to input the corresponding label and attribute to generate the needed testing data, moreover, the data link is complete, the accuracy is high, and the efficiency of automated generation is high.

Description

technical field [0001] The invention relates to data preparation and maintenance, in particular to a method and device for generating software test data. Background technique [0002] Most products nowadays, especially Internet products, use databases to save data. Each function of the product also depends on the corresponding business data, so in software testing, the preparation and maintenance of test data becomes a very important aspect. Especially for software systems with complex business logic, deep paths, and high coupling, when the test object is at the end of the business, it is time-consuming and labor-intensive to manually prepare the pre-service data that the test object depends on. Similarly, the preparation and management of test data is also a prerequisite for continuous integration and automated testing. Otherwise, due to business constraints, automated use cases cannot be executed repeatedly, and their meaning of existence will be lost. [0003] The metho...

AI Technical Summary

Problems solved by technology

All of these have their advantages and disadvantages. SQL generation requires testers to fully understand the business code and data structure, which is equivalent to reimplementing the business method with SQL, and the operation cost is high; the Docker container cannot provide enough sample data, because the data of the container itself needs to be prepared , the production traffic playback needs to keep the local test and production environment exactly the same, and the test data generation is mostly to serve the test of new requirements

The method of calling the business interface generation is the most reliable way, but because the business interface is independent, when the business scenario path is deep, a business scenario will cover a large number of interfaces, making manual maintenance difficult, and interface input parameters also need to be manually modified. Data generation efficiency is no different from manual operation

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