This is my first project using machine learning so I'm looking for some guidance. I am extending a model-based testing (MBT) system to a learning-based testing system by integrating a machine learning algorithm, for automation purposes. The MBT system executes tests on some system under test (SUT) and generates test verdicts.
The ML algorithm I want to integrate, is to take test verdicts from the MBT system as input and gain understanding of the SUT's behaviour, in order to generate new test cases. The test verdict (input) is a text-file, containing information about the previous test and whether it passed or failed. The output is a new test case, also a text file, containing variable values.
I was thinking that supervised learning would be suitable since the input file contains both variable values (features) and the test verdict (class). However, I have doubts since I am not looking to solve a classification problem.
I would appreciate ideas of what type of algorithm I should use (supervised/unsupervised/reinforcement etc.), and where I could find such (open-source) algorithms.