Model-Based Testing of Electronic Passports

Mostowski, Wojciech; Poll, Erik; Schmaltz, Julien; Tretmans, Jan; Schreur, Ronny Wichers

doi:10.1007/978-3-642-04570-7_19

Cited by 14 publications

(17 citation statements)

References 2 publications

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“…TorXakis [47] is another extension of the TorX model-based testing tool. In addition to the testing algorithms, TorXakis uses STS's with symbolic test generation to deal with structured data, i.e., symbols with data parameters [31], where TorX and JTorx use flattening.…”

Section: Model Based Testingmentioning

confidence: 99%

“…Constructing informative test queries (i.e., membership queries) is known as test selection and is one of the main problems dealt with in model-based software testing [44]. Several software testing methods and tools have been developed for this task in order to aid software development and maintainance, see, e.g., [14,47,51]. Although, these methods are able to approximate equivalence queries by (smartly) asking many membership queries, an exponential amount of them (or one of exponential length) are required in the worst case.…”

Section: Introductionmentioning

confidence: 99%

“…Using abstractions over input values through our Tomte learning tool [1] and the TorXakis [47], see Section 3. Abstractions can be seen as discretizations of the integer input values.…”

Section: Introductionmentioning

confidence: 99%

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Improving active Mealy machine learning for protocol conformance testing

et al. 2013

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Using a well-known industrial case study from the verification literature, the bounded retransmission protocol, we show how active learning can be used to establish the correctness of protocol implementation I relative to a given reference implementation R. Using active learning, we learn a model M R of reference implementation R, which serves as input for a model based testing tool that checks conformance of implementation I to M R . In addition, we also explore an alternative approach in which we learn a model M I of implementation I, which is compared to model M R using an equivalence checker. Our work uses a unique combination of software tools for model construction (Uppaal), active learning (LearnLib, Tomte), model-based testing (JTorX, TorXakis) and verification (CADP, MRMC). We show how these tools can be used for learning models of and revealing errors in implementations, present the new notion of a conformance oracle, and demonstrate how conformance oracles can be used to speed up conformance checking.

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Section: Model Based Testingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Improving active Mealy machine learning for protocol conformance testing

et al. 2013

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“…Model-based testing is a structured and rigorous discipline of testing, which is likely to improve the current practice of testing [24,23,17]. Input-output conformance (ioco) testing is a well-known formal approach to model-based testing, which is used extensively in various practical applications, see [13,5] and the references therein, and which has been the subject of much theoretical research, see [21] and the references therein.…”

Section: Introductionmentioning

confidence: 99%

On the Complexity of Input Output Conformance Testing

Noroozi

Mousavi

Willemse

2014

Formal Aspects of Component Software

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Abstract. Input-output conformance (ioco) testing is a well-known approach to model-based testing. In this paper, we study the complexity of checking ioco. We show that the problem of checking ioco is PSPACEcomplete. To provide a more efficient algorithm, we propose a more restricted setting for checking ioco, namely with deterministic models and show that in this restricted setting ioco checking can be performed in polynomial time.

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“…This speeds up the learning process and reduces model size. In contrast to a previous application of this technique [1,2] we validate our automatically derived model against a previous hand-made specification of the passport [13]. This specification was used to validate the Dutch implementation of the biometric passport using the ioco-theory for MBT [16].…”

Section: Introductionmentioning

confidence: 99%

Inference and Abstraction of the Biometric Passport

Aarts

Schmaltz

Vaandrager

2010

Lecture Notes in Computer Science

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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.

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Model-Based Testing of Electronic Passports

Cited by 14 publications

References 2 publications

Improving active Mealy machine learning for protocol conformance testing

Improving active Mealy machine learning for protocol conformance testing

On the Complexity of Input Output Conformance Testing

Inference and Abstraction of the Biometric Passport

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