Application functionality testing, resiliency testing, chaos testing, and performance testing using machine learning

A method comprising:

• receiving, by a processor, an instruction for testing a type of failure associated with a particular application;
• accessing, by the processor, a particular application model of the particular application, the particular application comprising a plurality of microservices that make up the particular application, wherein the particular application model represents a plurality of dependencies between or among the plurality of microservices;
• inputting, by the processor, the particular application model into a machine learning algorithm to generate a testing scenario comprising a plurality of tests for the particular application and timing information for applying the plurality of tests, wherein the machine learning algorithm is trained to predict, based on interactions between historic tests, testing scenarios that cause types of failures of applications;
• executing, by the processor, the testing scenario by applying the plurality of tests to the particular application according to the timing information;
• identifying, by the processor, based on the testing scenario applied to the particular application, one or more points of failure in one or more microservices of the plurality of microservices;
• and
• generating, by the processor, recommendations to revise code of the particular application based on the one or more points of failure.

One or more non-transitory, computer-readable media storing instructions that, when executed by one or more processors, cause operations comprising:

• receiving an instruction for testing a type of failure associated with a particular application;
• accessing a particular application model of the particular application, the particular application comprising a plurality of microservices that make up the particular application, wherein the particular application model represents a plurality of dependencies between or among the plurality of microservices;
• inputting the particular application model into a machine learning algorithm to generate a testing scenario comprising a plurality of tests for the particular application and timing information for applying the plurality of tests, wherein the machine learning algorithm is trained to predict, based on interactions between historic tests, testing scenarios that cause types of failures of applications;
• executing the testing scenario by applying the plurality of tests to the particular application according to the timing information;
• identifying based on the testing scenario applied to the particular application, one or more points of failure in one or more microservices of the plurality of microservices;
• and
• generating recommendations to revise code of the particular application based on the one or more points of failure.