Model Generation by Moderated Regular Extrapolation

This paper introduces regular extrapolation, a technique that provides descriptions of systems or system aspects a posteriori in a largely automatic way. The descriptions come in the form of models which offer the possibility of mechanically producing system tests, grading test suites and monitoring running systems. Regular extrapolation builds models from observations via techniques from machine learning and finite automata theory. Also expert knowledge about the system enters the model construction in a systematic way. The power of this approach is illustrated in the context of a test environment for telecommunication systems.