Inference and Abstraction of the Biometric Passport

Abstract

Abstract. 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.

Formal Models of Bank Cards for Free

“…This functionality is used for a so-called mapper component [5], [6] to be able to learn the transitions where a counter is increased. The mapper is integrated in the test harness of the SUT, and keeps track of the value of the counter.…”

“…The regular inference algorithm [3], [4] provides sequences of inputs to a System Under Test (SUT) and observes the responses to infer a Mealy machine, a special form of finite state machine, as explained in Section III. In addition to standard learning methods, abstraction techniques for data parameters [5], [6] can be used to learn a more detailed model of the system.…”

Improving active Mealy machine learning for protocol conformance testing

“…Tomte uses LearnLib as basic learning tool and therefore the abstraction of the implementation may not exhibit any nondeterminism: if it does then LearnLib crashes and Tomte has to refine the abstraction. Using Tomte, it is possible to learn fully automatically models of several realistic software components, including the biometric passport [4] as well as the SIP and TCP protocol [2].…”

“…To the best of our knowledge, this is a novel application area of grammatical inference and machine learning. Moreover, it is a promising one since reference implementations are in existence for many real-world software systems, but models are usually lacking or incomplete [2,4].…”

Demonstrating Learning of Register Automata

“…It has, e.g., been used to infer the behavior of a electronic passports [1], in security research [3], and it is a central enabler within the Connect framework.…”