SAT-based techniques for determining backbones for post-silicon fault localisation

Zhu, Charlie Shucheng; Weißenbacher, Georg; Sethi, Divjyot; Malik, Sharad

doi:10.1109/hldvt.2011.6113981

Cited by 21 publications

(17 citation statements)

References 18 publications

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“…The presentation begins with an implicant enumeration algorithm (based on Proposition 2) and it continues with an algorithm that tests one literal at a time (based on Proposition 3) [23]. Further, the section discusses an algorithm based on negating a backbone estimate [36]. A novel algorithm is introduced, which uses the notion of subsets of a backbone estimate (chunks) and it is shown that two of the previous algorithms are a special case of this novel one.…”

Section: Computing Backbonesmentioning

confidence: 99%

“…Identification of a backbone during the configuration process prevents the user from choosing values that cannot be extended to a model (or configuration). Another recent application of backbones is post-silicon fault localization in integrated circuits [35,36]. Manolios and Papavasileiou show that backbones enable improving the solving of pseudo-Boolean constraints via compilation to SAT [22].…”

Section: Introductionmentioning

confidence: 99%

“…For instance, inadmissible and necessary variables [17], bound literals [15], fixed assignments [36], units [22] or frozen variables [1]. Also, we should note that the concept of a backbone is closely related to the concept of failed literals.…”

Section: Introductionmentioning

confidence: 99%

“…(1) The article overviews recent algorithms for backbone computation [23,36]. (2) The article provides a unifying algorithm for backbone computation, and shows that some of the recent algorithms for backbone computation are special cases of this unifying algorithm.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Algorithms for computing backbones of propositional formulae

2015

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The problem of propositional satisfiability (SAT) has found a number of applications in both theoretical and practical computer science. In many applications, however, knowing a formula's satisfiability alone is insufficient. Often, some other properties of the formula need to be computed. This article focuses on one such property: the backbone of a formula, which is the set of literals that are true in all the formula's models. Backbones find theoretical applications in characterization of SAT problems and they also find practical applications in product configuration or fault localization. This article overviews existing algorithms for backbone computation and introduces two novel ones. Further, an extensive evaluation of the algorithms is presented. This evaluation demonstrates that one of the novel algorithms significantly outperforms the existing ones.

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Section: Computing Backbonesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Algorithms for computing backbones of propositional formulae

2015

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“…In 2011, based on the method of negating a literal per time then conjuncting it with the original formula to compute the backbone, C. Zhu proposed anti-literal algorithm, further boosting the iterative backbone algorithm [3] . In 2015, based on the idea of block and the core, Mikoláš Janota proposed the backbone algorithm with block and the core, improving the computation rate of backbone [4] .…”

Section: Introductionmentioning

confidence: 99%

Algorithm for Computing Backbones of Propositional Formulae Based on Solved Backbone Information

Wang¹,

Bao²,

Xing³

et al. 2016

Proceedings of the 2016 International Conference on Applied Mathematics, Simulation and Modelling

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Abstract-The problem of propositional satisfiability (SAT) has found a number of applications in both theoretical and practical computer science. However, in many applications, knowing a formulae's satisfiability alone is not enough to solve problems. Often, some other characteristics of formulae need to be known. In 1997, the definition of backbone occur -a set of variables which are true in any assignment of SAT, which starts the research of solution structure. Gradually, backbone has been recognized having great effect in rapidly solving intelligent planning and automated reasoning problems. First of all, this article reviews three existing algorithms for computing backbone, finding that no algorithm uses solved backbone as information. Based on this idea, we bring up a new algorithm for computing backbone of propositional formulae using solved backbone information. This article utilizes latest SAT competition data on original fastest algorithm (one test per time of 30 chuck) and the new algorithm. According to the experiment results, the new algorithm can have 10% advance on rate at the set of data which contain 80% or more chuck so it has practical application value.

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SMTBCF: Efficient Backbone Computing for SMT Formulas

Zhang

2019

Formal Methods and Software Engineering

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SAT-based techniques for determining backbones for post-silicon fault localisation

Cited by 21 publications

References 18 publications

Algorithms for computing backbones of propositional formulae

Algorithms for computing backbones of propositional formulae

Algorithm for Computing Backbones of Propositional Formulae Based on Solved Backbone Information

SMTBCF: Efficient Backbone Computing for SMT Formulas

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