Inference and abstraction of the biometric passport

F. Aarts, J. Schmaltz, F.W. Vaandrager

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/CongresprocedureConferentiebijdrageAcademicpeer review

50 Citaten (Scopus)

Samenvatting

Model-based testing is a promising software testing technique for the automation of test generation and test execution. One obstacle to its adoption is the difficulty of developing models. Learning techniques provide tools to automatically derive automata-based models. Automation is obtained at the cost of time and unreadability of the models. We propose an abstraction technique to reduce the alphabet and large data sets. Our idea is to extract a priori knowledge about the teacher and use this knowledge to define equivalence classes. The latter are then used to define a new and reduced alphabet. The a priori knowledge can be obtained from informal documentation or requirements. We formally prove soundness of our approach. We demonstrate the practical feasibility of our technique by learning a model of the new biometric passport. Our automatically learned model is of comparable size and complexity of a previous model manually developed in the context of testing a passport implementation. Our model can be learned within one hour and slightly refines the previous model.
Originele taal-2Engels
TitelLeveraging Applications of Formal Methods, Verification, and Validation (4th International Symposium on Leveraging Applications, ISoLA 2010, Heraklion, Crete, Greece, October 18-21, 2010. Proceedings, Part I)
RedacteurenT. Margaria, B. Steffen
Plaats van productieBerlin
UitgeverijSpringer
Pagina's673-686
ISBN van geprinte versie978-3-642-16557-3
DOI's
StatusGepubliceerd - 2010
Extern gepubliceerdJa

Publicatie series

NaamLecture Notes in Computer Science
Volume6415
ISSN van geprinte versie0302-9743

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