Model Based Testing meets Machine Learning

Rostislav Yavorskiy, School of Data Analysis and Artificial Intelligence
Higher School of Economics, Moscow

EXTENT Software Testing and Trading Technology Trends Meetup 2018

Transcript

1. Model Based Testing meets Machine Learning Rostislav Yavorskiy School of Data Analysis and Artificial Intelligence Higher School of Economics, Moscow
2. Computer Science at HSE 1. Software engineering 2. Applied mathematics and informatics 3. Applied data analysis … • Laboratory of Modeling and Control of Complex Systems 2
3. Model based testing 3 Models are used to represent the behavior of a system under test (SUT) A model describing a SUT is usually an abstract, partial presentation of the SUT's desired behavior. Often the model is a finite state transition system. Test cases derived from such a model are functional tests on the same level of abstraction as the model
4. Model based testing 4 ① ② ③ ④
5. Machine learning Machine learning uses statistical techniques to give computers the ability to "learn with data", i.e. progressively improve performance on a specific task without being explicitly programmed Machine learning is a method used to devise complex models and algorithms that are used for predictive analytics 5
6. Machine learning 6
7. Our strategy and plans Education - Summer/Winter Schools, Trainings Fundamental research - Regular research seminar, scientific conference, publications Collaborative projects with the industry - To land AI/ML/Blockchain/Chatbots and other fancy buzzwords into useful products and applications 7
8. Let’s collaborate … to turn software testers into data scientists! 8