Automatic generation of common asset validation tests for platform-based microservices

A method comprising:

• determining an asset test for validating a data asset for use by a first decision service, wherein the data asset is utilized by the first decision service for data processing of requests from requestor clients, and wherein the data asset is shared by a plurality of decision services;
• determining that a second decision service of the plurality of decision services has an existing asset test previously used to validate the data asset for use by the second decision service, wherein the existing asset test comprises test execution code that tests the data asset for a validation of the data asset for the second decision service;
• creating the asset test using the existing asset test and a machine learning (ML) model trained to adapt existing asset tests to validate data assets for the plurality of decision services, wherein the creating reconfigures the existing asset test for the first decision service based on test requirement parameters for the first decision service;
• and
• executing the asset test with the data asset for the first decision service, wherein the executing automatically validates whether the data asset is usable by the first decision service for the data processing of the requests from the requestor clients.

A non-transitory machine-readable medium having stored thereon machine-readable instructions executable to cause a machine to perform operations comprising:

• receiving a request to validate a data asset for a use by a first decision service of a plurality of decision services, wherein the data asset loads shared data for data processing requests from clients of an online service provider, and wherein the shared data is loaded from a data resource that is shared by the plurality of decision services;
• identifying an existing asset test previously used by a second decision service of the plurality of decision services to validate the data asset for a use by the second decision service;
• accessing an asset test generated from the existing asset test using a machine learning (ML) model trained to adapt the existing asset test to different ones of the plurality of decision services based on existing asset tests run by the plurality of decision services, wherein the asset test is generated by the ML model based on test execution code for the existing asset test and test requirement parameters for the first decision service;
• and
• testing the data asset for use by the first decision service using the generated asset test.