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This research will be part of the SUMBAT research project. SUMBAT integrates model based testing with automata learning. With model based testing we manually create a model of the system under test. This can be rather difficult, especially for legacy systems where specifications are missing, incomplete or incorrect.
Model learning is an approach where a model is derived from e.g. log files or other observations of the system. It will attempt to construct a model that corresponds to the observed behavior. Model learning is actively researched at the universities of Twente and Nijmegen. One of the leading tools is LearnLib.
Some of the topics for research in model learning are:
- Data. The current techniques have difficulty with systems that use data.
- Non-determinism. The current techniques have difficulty with systems that exhibit non-deterministic behavior. Most of the complex real-world systems have this property.
- The same holds for time.
- The types of models that are learned are different than the models that Axini uses. At Axini we use Symbolic Transition Systems that support time and data. These models have pleasant compositional properties to form bigger models by combining smaller models (for example in sequential or parallel composition). The models that LearnLib supports are for example Mealy machines and Finite Automata. These do not share these compositional properties. It would be nice to develop theory and tools that support Transition Systems.
Possible research questions:
- How can we extend the current theory to support data, time and/or non-determinism.
- Is it possible for the theory underlying LearnLib to support data, time and non-determinism. Or should we develop a new theory. There are alternative theories that do not suffer from the problems that LearnLib has.
- Can we apply LearnLib to systems from our customers? For example, ProRail, Achmea or ITAB?