Using Model Counting to Find Optimal Distinguishing Tests

Stefan, Heinz G.; Sachenbacher, Martin

doi:10.1007/978-3-642-01929-6_10

Cited by 5 publications

(8 citation statements)

References 11 publications

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“…We solve a different problem than that of Heinz and Sachenbacher (2008), Alur, Courcoubetis, and Yannakakis (1995). Both of these approaches assume a non-deterministic model defined as an automaton.…”

Section: Related Workmentioning

confidence: 99%

“…In contrast, our framework assumes a static system (plant model) for which we must compute a temporal sequence of tests to best isolate the diagnosis. Esser and Struss (2007) also adopt an automaton framework for test generation, except that, unlike Heinz and Sachenbacher (2008) or Alur et al (1995), they transform this automaton to a relational specification, and apply their framework to software diagnosis. This automaton-based framework accommodates more general situations than does ours, such as the possibility that the system's state after a transition may not be uniquely determined by the state before the transition and the input, and/or the system's state may be associated with several possible observations.…”

Section: Related Workmentioning

confidence: 99%

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A Model-Based Active Testing Approach to Sequential Diagnosis

Feldman

Provan

Gemund

2010

jair

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Model-based diagnostic reasoning often leads to a large number of diagnostic hypotheses. The set of diagnoses can be reduced by taking into account extra observations (passive monitoring), measuring additional variables (probing) or executing additional tests (sequential diagnosis/test sequencing). In this paper we combine the above approaches with techniques from Automated Test Pattern Generation (ATPG) and Model-Based Diagnosis (MBD) into a framework called Fractal (FRamework for ACtive Testing ALgorithms). Apart from the inputs and outputs that connect a system to its environment, in active testing we consider additional input variables to which a sequence of test vectors can be supplied. We address the computationally hard problem of computing optimal control assignments (as defined in Fractal) in terms of a greedy approximation algorithm called Fractal G . We compare the decrease in the number of remaining minimal cardinality diagnoses of Fractal G to that of two more Fractal algorithms: Fractal ATPG and Fractal P . Fractal ATPG is based on ATPG and sequential diagnosis while Fractal P is based on probing and, although not an active testing algorithm, provides a baseline for comparing the lower bound on the number of reachable diagnoses for the Fractal algorithms. We empirically evaluate the trade-offs of the three Fractal algorithms by performing extensive experimentation on the ISCAS85/74XXX benchmark of combinational circuits.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

A Model-Based Active Testing Approach to Sequential Diagnosis

Feldman

Provan

Gemund

2010

jair

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“…The intuition is that if we assume the possible outcomes (feasible assignments to the output variables) to be (roughly) equally likely, a PDT will be more likely to distinguish among two given hypotheses compared to another PDT, if the ratio of possible outcomes that are unique to a hypothesis versus all possible outcomes is higher. The notion of optimal distinguishing tests introduced in [8] formalizes this goal of finding tests that discriminate among two hypotheses as good as possible:…”

Section: Optimal Distinguishing Testsmentioning

confidence: 99%

“…Finding ODTs is important as it reduces the number of tests to be executed and the overall costs of the testing process. [8] proposed and analyzed a simple greedy-type algorithm to approximate ODTs, which in some real-world applications produces test inputs whose distinguishing ratios are close to those of ODTs.…”

Section: Introductionmentioning

confidence: 99%

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Constraint-Based Optimal Testing Using DNNF Graphs

Schumann

Sachenbacher

Huang

2009

Principles and Practice of Constraint Programming - CP 2009

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The goal of testing is to distinguish between a number of hypotheses about a system-for example, different diagnoses of faultsby applying input patterns and verifying or falsifying the hypotheses from the observed outputs. Optimal distinguishing tests (ODTs) are those input patterns that are most likely to distinguish between hypotheses about non-deterministic systems. Finding ODTs is practically important, but it amounts in general to determining a ratio of model counts and is therefore computationally very expensive. In this paper, we present a novel approach to constraint-based ODT generation, which uses structural properties of the system to limit the complexity of computation. We first construct a compact graphical representation of the testing problem via compilation into decomposable negation normal form. Based on this compiled representation, we show how one can evaluate distinguishing tests in linear time, which allows us to efficiently determine an ODT. Experimental results from a real-world application show that our method can compute ODTs for instances that were intractable for previous approaches.

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