Regular Extrapolation of Behavioural Models-Searching for Regular Patterns by Observation

Опубликовано 6 сентября 2016, 5:44

In current practice, only rarely precise and reliable documentation of a system's behaviour is produced during its development. Revisions and last minute changes invalidate design sketches, and while systems are updated in the maintenance cycle, often their implementation documentation is not. Regular extrapolation aims at providing descriptions of systems or system aspects a posteriori in a largely automatic way. These descriptions come in the form of models which offer the possibility of mechanically producing system tests, grading test suites and monitoring running systems. The models are built 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 praticality of this approach is discussed in the context of a learning scenario for telecommunication systems