Assume that I have a large software application, this application is event driven i.e. when a user configures something by pressing on a button a code flow executes.
Along with this application I also have a system which can emulate a user and can perform all permutation of steps which a user might perform. Hence it is safe to say that I can bring my software application to all the possible states. I have already done the automation for this, I just need a brain which can say that the current state is buggy (undesired) or not.
Now the question is:
Is it possible to determine if my system has gone into an unexpected state (Bug) automatically via machine learning?
At first I thought of using Rete Net to solve this. Idea was to make a knowledge base which will emulate a tester and can tell if the system is in an inconsistent state or not, but for this I need to constantly feed the knowledge base with new rules whenever a new feature is added to this software application.