Domain-specific optimization in automata learning. (English) Zbl 1278.68177

Hungar, Hardi; Niese, Oliver; Steffen, Bernhard

Hunt, Warren A.jun. (ed.) et al., Computer aided verification. 15th international conference, CAV 2003, Boulder, CO, USA, July 8–12, 2003. Proceedings. Berlin: Springer (ISBN 3-540-40524-0/pbk). Lect. Notes Comput. Sci. 2725, 315-327 (2003).

Summary: Automatically generated models may provide the key towards controlling the evolution of complex systems, form the basis for test generation and may be applied as monitors for running applications. However, the practicality of automata learning is currently largely preempted by its extremely high complexity and unrealistic frame conditions. By optimizing a standard learning method according to domain-specific structural properties, we are able to generate abstract models for complex reactive systems. The experiments conducted using an industry-level test environment on a recent version of a telephone switch illustrate the drastic effect of our optimizations on the learning efficiency. From a conceptual point of view, the developments can be seen as an instance of optimizing general learning procedures by capitalizing on specific application profiles.
For the entire collection see [Zbl 1027.00031].

MSC:

68Q60	Specification and verification (program logics, model checking, etc.)
68Q32	Computational learning theory
68Q45	Formal languages and automata
90B25	Reliability, availability, maintenance, inspection in operations research