Testing of Computing Processes Using Artificial Intelligence

A system to automatically generate tests to execute on a process, comprising:

• a data store of input operation requests for the process, which have been recorded as at least one user utilizes functionality of the process;
• a deep neural network model that is trained using the input operation requests;
• a test generator to generate one or more test series using the deep neural network model, wherein the one or more test series are executable to activate functionality of the process and to test portions of the process;
• and
• a test validator to validate the one or more test series with a machine learning model used as a classifier to classify whether a test series is executable on the process;
• and
• a test executor to execute validated test series to test functionality of the process.

A non-transitory computer-readable medium comprising computer-executable instructions which implement automatic generation of tests to execute on a process, comprising:

• identifying a data store of input operation requests for the process, which have been recorded as requests are received to operate functionality of the process;
• training a deep neural network model using the input operation requests to enable the deep neural network model to generate output series based in part on the input operation requests;
• generating one or more test series using the deep neural network model, wherein the test series are executable to activate functionality of the process in order to test portions of the process;
• executing the one or more test series on the process in order to test functionality of the process;
• receiving test output from the process in response to the one or more test series;
• processing test output with a machine learning model used as classifier to determine that the test output represents valid behavior of the process;
• and
• reporting when the test output has a set of invalid behavior.