Abstract
We present the effects of using an efficient algorithm for behavior-based model synthesis which is specifically tailored to reactive (legacy) system behaviors. Conceptual backbone is the classical automata learning procedure L*, which we adapt according to the considered application profile. The resulting learning procedure LMealy*, which directly synthesizes generalized Mealy automata from behavioral observations gathered via an automated test environment, drastically outperforms the classical learning algorithm for deterministic finite automata. Thus it marks a milestone towards opening industrial legacy systems to model-based test suite enhancement, test coverage analysis, and online testing.
Original language | English |
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Pages (from-to) | 95-100 |
Number of pages | 6 |
Journal | Proceedings - IEEE International High-Level Design Validation and Test Workshop, HLDVT |
DOIs | |
Publication status | Published - 2004 |
Externally published | Yes |
Event | Proceedings - Ninth IEEE International High-Level Design Validation and Test Workshop, HLDVT'04 - Sonoma Valley, CA, United States Duration: 10 Nov 2004 → 12 Nov 2004 |