Source code and data relevant for the paper 'Learning Register Automata with Fresh Value Generation'
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The dataset contains source code and data relevant for the journal extension of the conference paper "Learning Register Automata with Fresh Value Generation". The journal was due submission at the moment the dataset was made.The journal extension: http://www.sws.cs.ru.nl/publications/papers/fvaan/TomteFresh/main.pdfConference version: https://link.springer.com/chapter/10.1007/978-3-319-25150-9_11The dataset also contains some data relevant to an earlier conference paper "'Algorithms for Inferring Register Automata" available at: https://link.springer.com/chapter/10.1007/978-3-662-45234-9_15In this work, we propose a learning algorithm for register automata and Tomte, a tool implementing this algorithm. Our approach is based on the concept of learning with abstraction, where concrete parameters are abstracted away by a mapper component. Our learning algorithm automatically derives the mapper as well as the abstract Mealy machine model. From these two we can obtain a concrete register automata model. Peculiar to our approach is that we allow register automata to generate fresh values (arbitrary values). This improves applicability as many real-world system exhibit fresh values.We …
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