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 $L_1$ 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.

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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