Inference and Abstraction of the Biometric Passport

Fides Aarts, Julien Schmaltz and Frits Vaandrager. Inference and Abstraction of the Biometric Passport. In T. Margaria and B. Steffen, editors. Proceedings 4th International Symposium On Leveraging Applications of Formal Methods, Verification and Validation (ISoLA 2010), 18-20 October 2010 - Amirandes, Heraclion, Crete. LNCS 6415, pp. 673-686, Springer-Verlag, 2010.


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.

Paper and Models

local copy of paper
published version
Learned model (.dot file)
LTS specification (.aut Aldebaran format)
LTS implementation (.aut Aldebaran format)