Improving Model Inference of Black Box Components having Large Input Test Set

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Muhammad Naeem Irfan, Roland Groz, Catherine Oriat ;
Proceedings of the Eleventh International Conference on Grammatical Inference, PMLR 21:133-138, 2012.

Abstract

The deterministic finite automata (DFA) learning algorithm \emphL* has been extended to learn Mealy machine models which are more succinct for \emphinput/output (i/o) based systems. We propose an optimized learning algorithm \emphL_1 to infer Mealy models of software black box components. The \emphL_1 algorithm uses a modified observation table and avoids adding unnecessary elements to its columns and rows. The proposed improvements reduce the worst case time complexity. The \emphL_1 algorithm is compared with the existing Mealy inference algorithms and the experiments conducted on a comprehensive set confirm the gain.

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