Coverage Criteria for Set-Based Specifications

Model-based testing (MBT) studies how test cases are generated from a model of the system under test (SUT). Many MBT methods rely on building an automaton from the model and then they generate test cases by covering the automaton with different path coverage criteria. However, if a model of the SUT is a logical formula over some complex mathematical theories (such as set theory) it may be more natural or intuitive to apply coverage criteria directly over the formula. On the other hand, domain partition, i.e. the partition of the input domain of model operations, is one of the main techniques in MBT. Partitioning is conducted by applying different rules or heuristics. Engineers may find it difficult to decide what, where and how these rules should be applied. In this paper we propose a set of coverage criteria based on domain partition for set-based specifications. We call them testing strategies. Testing strategies play a similar role to path- or data-based coverage criteria in structural testing. Furthermore, we show a partial order of testing strategies as is done in structural testing. We also describe an implementation of testing strategies for the Test Template Framework, which is a MBT method for the Z notation; and a scripting language that allows users to implement testing strategies

Ioco theory for probabilistic automata

Model-based testing (MBT) is a well-known technology, which allows for automatic test case generation, execution and evaluation. To test non-functional properties, a number of test MBT frameworks have been developed to test systems with real-time, continuous behaviour, symbolic data and quantitative system aspects. Notably, a lot of these frameworks are based on Tretmans' classical input/output conformance (ioco) framework. However, a model-based test theory handling probabilistic behaviour does not exist yet. Probability plays a role in many different systems: unreliable communication channels, randomized algorithms and communication protocols, service level agreements pinning down up-time percentages, etc. Therefore, a probabilistic test theory is of great practical importance. We present the ingredients for a probabilistic variant of ioco and define the {\pi}oco relation, show that it conservatively extends ioco and define the concepts of test case, execution and evaluation

Parallel algorithms for generating distinguishing sequences for observable non-deterministic FSMs

A distinguishing sequence (DS) for a finite state machine (FSM) is an input sequence that distinguishes every pair of states of the FSM. There are techniques that generate a test sequence with guaranteed fault detection power and it has been found that shorter test sequence can be produced if DSs are used. Despite these benefits, however, until recently the only published DS generation algorithms have been for deterministic FSMs. This paper develops a massively parallel algorithm, which can be used in GPU Computing, to generate DSs from partial observable non-deterministic FSMs. We also present the results of experiments using randomly generated FSMs and some benchmark FSMs. The results are promising and indicate that the proposed algorithm can derive DSs from partial observable non-deterministic FSMs with 32,000 states in an acceptable amount of time.This work is supported by the Scientific and Technological Research Council of Turkey (TUBITAK) under Grant #1059B191400424 and by the NVIDIA corporation

Bisimilar States in Uncertain Structures

We provide a categorical notion called uncertain bisimilarity, which allows to reason about bisimilarity in combination with a lack of knowledge about the involved systems. Such uncertainty arises naturally in automata learning algorithms, where one investigates whether two observed behaviours come from the same internal state of a black-box system that can not be transparently inspected. We model this uncertainty as a set functor equipped with a partial order which describes possible future developments of the learning game. On such a functor, we provide a lifting-based definition of uncertain bisimilarity and verify basic properties. Beside its applications to Mealy machines, a natural model for automata learning, our framework also instantiates to an existing compatibility relation on suspension automata, which are used in model-based testing. We show that uncertain bisimilarity is a necessary but not sufficient condition for two states being implementable by the same state in the black-box system. We remedy the lack of sufficiency by a characterization of uncertain bisimilarity in terms of coalgebraic simulations

Dagstuhl News January - December 2008

"Dagstuhl News" is a publication edited especially for the members of the Foundation "Informatikzentrum Schloss Dagstuhl" to thank them for their support. The News give a summary of the scientific work being done in Dagstuhl. Each Dagstuhl Seminar is presented by a small abstract describing the contents and scientific highlights of the seminar as well as the perspectives or challenges of the research topic

With a little help from your friends: semi-cooperative games via Joker moves

This paper coins the notion of Joker games where Player 2 is not strictly adversarial: Player 1 gets help from Player 2 by playing a Joker. We formalize these games as cost games, and study their theoretical properties. Finally, we illustrate their use in model-based testing.Comment: Extended version with appendi