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

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 L∗ has been extended to learn Mealy machine models which are more succinct for \emph{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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v21-irfan12a, title = {Improving Model Inference of Black Box Components having Large Input Test Set}, author = {Irfan, Muhammad Naeem and Groz, Roland and Oriat, Catherine}, booktitle = {Proceedings of the Eleventh International Conference on Grammatical Inference}, pages = {133--138}, year = {2012}, editor = {Heinz, Jeffrey and Higuera, Colin and Oates, Tim}, volume = {21}, series = {Proceedings of Machine Learning Research}, address = {University of Maryland, College Park, MD, USA}, month = {05--08 Sep}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v21/irfan12a/irfan12a.pdf}, url = {https://proceedings.mlr.press/v21/irfan12a.html}, abstract = {The deterministic finite automata (DFA) learning algorithm $L*$ has been extended to learn Mealy machine models which are more succinct for \emph{input/output} (i/o) based systems. We propose an optimized learning algorithm $L_1$ to infer Mealy models of software black box components. The $L_1$ 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 $L_1$ algorithm is compared with the existing Mealy inference algorithms and the experiments conducted on a comprehensive set confirm the gain.} }
Endnote
%0 Conference Paper %T Improving Model Inference of Black Box Components having Large Input Test Set %A Muhammad Naeem Irfan %A Roland Groz %A Catherine Oriat %B Proceedings of the Eleventh International Conference on Grammatical Inference %C Proceedings of Machine Learning Research %D 2012 %E Jeffrey Heinz %E Colin Higuera %E Tim Oates %F pmlr-v21-irfan12a %I PMLR %P 133--138 %U https://proceedings.mlr.press/v21/irfan12a.html %V 21 %X The deterministic finite automata (DFA) learning algorithm $L*$ has been extended to learn Mealy machine models which are more succinct for \emph{input/output} (i/o) based systems. We propose an optimized learning algorithm $L_1$ to infer Mealy models of software black box components. The $L_1$ 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 $L_1$ algorithm is compared with the existing Mealy inference algorithms and the experiments conducted on a comprehensive set confirm the gain.
RIS
TY - CPAPER TI - Improving Model Inference of Black Box Components having Large Input Test Set AU - Muhammad Naeem Irfan AU - Roland Groz AU - Catherine Oriat BT - Proceedings of the Eleventh International Conference on Grammatical Inference DA - 2012/08/16 ED - Jeffrey Heinz ED - Colin Higuera ED - Tim Oates ID - pmlr-v21-irfan12a PB - PMLR DP - Proceedings of Machine Learning Research VL - 21 SP - 133 EP - 138 L1 - http://proceedings.mlr.press/v21/irfan12a/irfan12a.pdf UR - https://proceedings.mlr.press/v21/irfan12a.html AB - The deterministic finite automata (DFA) learning algorithm $L*$ has been extended to learn Mealy machine models which are more succinct for \emph{input/output} (i/o) based systems. We propose an optimized learning algorithm $L_1$ to infer Mealy models of software black box components. The $L_1$ 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 $L_1$ algorithm is compared with the existing Mealy inference algorithms and the experiments conducted on a comprehensive set confirm the gain. ER -
APA
Irfan, M.N., Groz, R. & Oriat, C.. (2012). Improving Model Inference of Black Box Components having Large Input Test Set. Proceedings of the Eleventh International Conference on Grammatical Inference, in Proceedings of Machine Learning Research 21:133-138 Available from https://proceedings.mlr.press/v21/irfan12a.html.

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