Automated testing method, device, electronic device and computer readable medium

Abstract

The disclosure relates to an automatic testing method, device, electronic equipment and computer readable medium of a medical data platform. Involving in the field of medical information processing, the method includes: establishing configuration information and scene models according to disease names and disease characteristics; extracting data that meets predetermined conditions from patient data to generate a data model; extracting corresponding interface information according to the functions of the medical data platform, and generating execution use cases according to the interface information and parameter templates; and automatically testing the medical data platform through the scenario model, the data model, and the execution use cases. The medical data platform automated testing method, device, electronic equipment and computer readable medium involved in the present disclosure can ensure the product quality of the medical data platform through targeted data and functional testing.

Description

technical field [0001] The present disclosure relates to the field of computer information processing, in particular, to a medical data platform automated testing method, device, electronic equipment and computer readable medium. Background technique [0002] Interface testing is often used to test the functional interfaces exposed by the server. This kind of interface testing is more biased towards the testing of functional functions, providing normal values ​​and boundary values ​​to verify the performance of the interface under different parameters; in big data analysis, Overall, it is more inclined to sort and classify data, give distribution and statistics in different situations, and face medical big data testing. The amount of medical data and field data is huge, and it is difficult to provide data with simple analysis and match functional interfaces to verify data. Simple interface testing is a common solution to the impact on the performance of front-end functions, ...

AI Technical Summary

Problems solved by technology

[0002] Interface testing is often used to test the functional interfaces exposed by the server. This kind of interface testing is more biased towards the testing of functional functions, providing normal values ​​and boundary values ​​to verify the performance of the interface under different parameters; in big data analysis, Overall, it is more inclined to sort and classify data, give distribution and statistics in different situations, and face medical big data testing. The amount of medical data and field data is huge, and it is difficult to provide data with simple analysis and match functional interfaces to verify data. Simple interface testing is a common solution to the impact on the performance of front-end functions, but due to the huge amount of data, it is difficult and costly to cover all situations

[0003] At present, the commonly used method is to use front-end automation or interface automation to cover whether the performance of front-end functions is normal. Front-end automation is more inclined to verify the performance of page functions. Clicking on different functions of the page by simulating coordinate clicks can be effective. Verify the effectiveness and functional performance of the front-end response in an automated way, but each function needs to be written manually, and when the page style changes, the follow-up maintenance cost of this method is very high. If it involves large-scale changes, it is equivalent to writing before The previous ones are no longer applicable and need to be rewritten, and cannot be fully verified at the data level; another way is to verify the front-end function performance by testing the interface provided by the server to the front-end, which can be used to a large extent It solves the impact of front-end style changes and reduces maintenance costs to a certain extent. However, for interfaces, it generally only verifies different types of input parameters to observe boundary conditions and normal performance. However, in the field of medical big data, the data is complex and a large number of Different data may lead to different performance of the interface, resulting in unstable front-end performance

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