Uraz Cengiz Türker

Distinguishing Sequences for Partially Specified FSMs

Distinguishing Sequences DSs are used inmany Finite State Machine FSM based test techniques. Although Partially Specified FSMs PSFSMs generalise FSMs, the computational complexity of constructing Adaptive and Preset DSs ADSs/PDSs for PSFSMs has not been addressed. This paper shows that it is possible to check the existence of an ADS in polynomial time but the corresponding problem for PDSs is PSPACE-complete. We also report on the results of experiments with benchmarks and over 8 i¾? 106 PSFSMs.

Hardness and inapproximability of minimizing adaptive distinguishing sequences

An adaptive distinguishing sequence (ADS) can be used for identifying an unknown initial state of a finite state machine (FSM). It has been long known that checking the existence of an ADS for an FSM, and finding an ADS for an FSM when one exists, can be performed in polynomial time. However, the problem of finding a minimum ADS has not been studied so far. Generating a minimum ADS is especially motivated when such an ADS is used repeatedly, e.g. for the construction of a test sequence. We introduce a number of metrics to define a minimum ADS and show that the problem of generating a minimum ADS with respect to these metrics is NP-complete. In addition, we provide inapproximability results for these hard problems and show that not only deciding but also approximating such a minimum ADS is a hard problem. We modify the only polynomial time ADS generation algorithm existing, and experimentally show that these modifications construct reduced ADSs. We also validate the motivation of ADS minimization by presenting experimental results on the effect of using reduced ADSs to generate test sequences.

Incomplete Distinguishing Sequences for Finite State Machines

Given a Finite State Machine (FSM) M , a Distinguishing Sequence (DS) is a test that identifies the state of M . While there are two types of DSs, preset DSs (PDSs) and adaptive DSs (ADSs), not all FSMs possess a DS. In this paper, we examine the problem of finding incomplete PDSs and ADSs, exploring associated optimisation problems: finding a largest set of states that has a DS and finding a smallest set of DSs that, between them, distinguish all of the states. We also propose a greedy algorithm to produce a small set of incomplete ADSs and use experiments to compare this with two previously published algorithms for generating state identifiers. We show that the optimisation problems related to incomplete ADSs and PDSs are PSPACE-complete as are corresponding approximation problems. In the experiments we found that incomplete ADSs produced by the proposed greedy algorithm led to relatively compact state identifiers.

Lookahead-Based Approaches for Minimizing Adaptive Distinguishing Sequences

For Finite State Machine (FSM) based testing, it has been shown that the use of shorter Adaptive Distinguishing Sequences (ads) yields shorter test sequences. It is also known, on the other hand, that constructing a minimum cost ADS is an NP-hard problem and it is NP-hard to approximate. In this paper, we introduce a lookahead-based greedy algorithm to construct reduced ADSs for FSMs. The greedy algorithm inspects a search space to make a decision. The size of the search space is adjustable, allowing a trade-off between the quality and the computation time. We analyse the performance of the approach on randomly generated FSMs by comparing the ADSs constructed by our algorithm with the ADSs that are computed by the existing algorithms.

The relation between preset distinguishing sequences and synchronizing sequences

We study the relation between synchronizing sequences and preset distinguishing sequences which are some special sequences used in finite state machine based testing. We show that the problems related to preset distinguishing sequences can be converted into related problems of synchronizing sequences. Using the results existing in the literature for synchronizing sequences, we offer several reflections of these results for preset distinguishing sequences. Although computing a preset distinguishing sequence is PSPACE-hard , we do identify a class of machines for which computing a preset distinguishing sequence can be performed in polynomial time and argue that this class is practically relevant. We also present an experimental study to compare the performance of exponential brute-force and polynomial heuristic algorithms to compute a preset distinguishing sequence.

Generating Preset Distinguishing Sequences Using SAT

The preset distinguishing sequence generation problem is converted into a SAT problem to investigate the performance of SAT solvers for generating preset distinguishing sequences. An initial set of experiments are carried out and it is shown that the heuristics of SAT solvers can perform better than brute force algorithms that are used to generate preset distinguishing sequences.

A visualisation technique for large temporal social network datasets in Hyperbolic space

Visualisations of temporal social network datasets have the potential to be complex and require a lot of cognitive input. In this paper, we present a novel visualisation approach that depicts both relational and statistical information of evolving social structures. The underlying framework is implemented by the usage of Hyperbolic Geometry to support focus context rendering. The proposed method guarantees representing prominent social actors through scaling their representations, preserves users mental map, and provides the user to reduce visual clutter by means of filtering.

Effective algorithms for constructing minimum cost adaptive distinguishing sequences

Context: Given a Finite State Machine (FSM), a checking sequence is a test sequence that determines whether the system under test is correct as long as certain standard assumptions hold. Many checking sequence generation methods use an adaptive distinguishing sequence (ADS), which is an experiment that distinguishes the states of the specification machine. Furthermore, it has been shown that the use of shorter ADSs yields shorter checking sequences. It is also known, on the other hand, that constructing a minimum cost ADS is an NP-hard problem and it is NP-hard to approximate. This motivates studying and investigating effective ADS construction methods.Objective: The main objective of this paper is to suggest new methods that can compute compact ADSs to be used in the construction of checking sequences.Method: We briefly present the existing ADS construction algorithms. We then propose generalizations of these approaches with a set of heuristics. We also conduct experiments to compare the size of the resultant ADSs and the length of the checking sequences constructed using these ADSs.Results: The results indicate that when the ADSs are constructed with the proposed methods, the length of the checking sequences may reduce up to 54% (40% on the average).Conclusions: In this paper, we present the state of the art ADS construction methods for FSMs and we propose generalizations of these methods. We show that our methods are effective in terms of computation time and ADS quality.

Distinguishing Sequences for Distributed Testing: Preset Distinguishing Sequences

This paper concerns the problem of testing from a finite state machine (FSM)

An Improved Upper Bound for the Length of Preset Distinguishing Sequences of Distinguished Merging Finite State Machines

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