Schedule: 25 November 2020, 16:30 GMT, 17:30 Italian time
Virtual link: TBA
Speaker: Martina Maggio, Saarland University, Germany
Title: Testing Adaptive Software with Probabilistic GuaranteesAbstract: Testing software that adapts, like a machine learning algorithm, is very complicated. In most cases, it is very difficult – if not impossible – to conduct exhaustive testing and analyse each possibile configuration. This is not only because the space of the configurations is very large, but also because the software learns and adapts, and running the same function with the same set of inputs may result in different outcomes.
In this context, it is impossible to get a deterministic answer to the software correctness, and there is a need for a paradigm shift to the probabilistic setup. In our research, we explored different alternatives to obtain probabilistic guarantees. The classical tools from statistics are Monte Carlo simulations and the Extreme Value Theory. We show that these tools have limitations that can be overcame by formulating the problem of testing a software that adapts itself as a chance-constrained optimization problem. In doing so, we employ the scenario theory, from the field of robust control.
Joint CS@GSSI/ICE-TCS@Reykjavik University virtual seminar — speaker: Martina Maggio