Machine Learning-Assisted Performance Testing

Automated testing activities like automated test case generation imply a reduction in human effort and cost, with the potential to impact the test coverage positively. If the optimal policy, i.e., the course of actions adopted, for performing the intended test activity could be learnt by the testing system, i.e., a smart tester agent, then the learnt policy could be reused in analogous situations which leads to even more efficiency in terms of required efforts. Performance testing under stress execution conditions, i.e., stress testing, which involves providing extreme test conditions to find the performance breaking points, remains a challenge, particularly for complex software systems. Some common approaches for generating stress test conditions are based on source code or system model analysis, or use-case based design approaches. However, source code or precise system models might not be easily available for testing. Moreover, drawing a precise performance model is often difficult, particularly for complex systems. In this research, I have used model-free reinforcement learning to build a self-adaptive autonomous stress testing framework which is able to learn the optimal policy for stress test case generation without having a model of the system under test. The conducted experimental analysis shows that the proposed smart framework is able to generate the stress test conditions for different software systems efficiently and adaptively without access to performance models.