Improving Model Inference of Black Box Components having Large Input Test Set - Archive ouverte HAL Accéder directement au contenu
Communication Dans Un Congrès Journal of Machine Learning Research Année : 2012

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

Résumé

The deterministic finite automata (DFA) learning algorithm L* has been extended to learn Mealy machine models which are more succinct for input/output (i/o) based systems. We propose an optimized learning algorithm L1 to infer Mealy models of software black box components. The L1 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 L1 algorithm is compared with the existing Mealy inference algorithms and the experiments conducted on a comprehensive set confirm the gain.
Fichier non déposé

Dates et versions

hal-00857279 , version 1 (03-09-2013)

Identifiants

  • HAL Id : hal-00857279 , version 1

Citer

Muhammad Naeem Irfan, Roland Groz, Catherine Oriat. Improving Model Inference of Black Box Components having Large Input Test Set. ICGI 2012 - 11th International Conference on Grammatical Inference, Sep 2012, College Park, MD, United States. pp.133-138. ⟨hal-00857279⟩
110 Consultations
0 Téléchargements

Partager

Gmail Facebook X LinkedIn More