Model Learning: Generating Automata Models from Tests
Speaker:
Bengt Jonsson
Date and Time
Friday, Nov 24th, 2017 at 14:15.
Location
Polacksbacken, ITC, room 1245.
Abstract
Model-based approaches to development, verification, and testing are becoming increasingly important for efficient development of reliable software Its application is hampered by a lack of adequate specifications for software components, libraries, and services. This problem is addressed by the area of Model Learning, also known under names such as "Specification Mining" or "Test-Based Modeling". More technically, and in this context, Model Learning consists of techniques for generating automata models from outcomes of tests on a black-box component. This presentation will review some basic principles of model learning, and present an overview of recent results and work in progress by the Uppsala team (also in collaboration with the groups of Bernhard Steffen (TU Dortmund) and Frits Vaandrager (U Nijmegen)). We present how results on learning finite-state models can be extended to the learning of infinite-state models, e.g., to capture the influence of data value on the dynamic behavior of a component, or the influence of timers. We also survey some recent applications of this generalization to learning models of, communication protocols and library components.