Dual Hashing-Based Algorithms for Discrete Integration

Colnet, Alexis de; Meel, Kuldeep S.

doi:10.1007/978-3-030-30048-7_10

Cited by 2 publications

(2 citation statements)

References 13 publications

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“…To accomplish an efficient procedure, we need to tackle three challenges: Figure 1: Feature model of graph library Challenge 1: Representation of weights over assignment space To represent weights over assignment space, we turn to a literal-weighted function that assigns a non-negative weight to every literal such that the weight of an assignment is the product of the weight of its literal. The choice of literalweighted function is primarily motivated due to the observation that a wide variety of distributions arising from diverse disciplines can be represented as literal-weight function [13,20].…”

Section: Adaptive Weighted Samplingmentioning

confidence: 99%

Baital: an adaptive weighted sampling approach for improved t-wise coverage

Baranov

Legay

Meel

2020

Proceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Softw

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The rise of highly configurable complex software and its widespread usage requires design of efficient testing methodology. t-wise coverage is a leading metric to measure the quality of the testing suite and the underlying test generation engine. While uniform samplingbased test generation is widely believed to be the state of the art approach to achieve t-wise coverage in presence of constraints on the set of configurations, such a scheme often fails to achieve high t-wise coverage in presence of complex constraints. In this work, we propose a novel approach Baital, based on adaptive weighted sampling using literal weighted functions, to generate test sets with high t-wise coverage. We demonstrate that our approach reaches significantly higher t-wise coverage than uniform sampling. The novel usage of literal weighted sampling leaves open several interesting directions, empirical as well as theoretical, for future research. CCS CONCEPTS • Software and its engineering → Feature interaction; Software product lines; Software testing and debugging.

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Section: Adaptive Weighted Samplingmentioning

confidence: 99%

Baital: an adaptive weighted sampling approach for improved t-wise coverage

Baranov

Legay

Meel

2020

Proceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Softw

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“…The unavailability of MaxSAT solvers with native support for XORs has hindered the scalability of WISH style techniques. A dual approach proposed by Chakraborty et al [8,16] reduces discrete integration to linearly many counting queries over formulas wherein each of counting query is constructed by conjunction of F with Pseudo-Boolean constraints, which is shown to scale poorly [38]. In summary, there exists a wide gap in the theory and practice of hashing-based approaches for discrete integration.…”

Section: Related Workmentioning

confidence: 99%

Taming Discrete Integration via the Boon of Dimensionality

Dudek,

Fried,

Meel

2020

Preprint

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Discrete integration is a fundamental problem in computer science that concerns the computation of discrete sums over exponentially large sets. Despite intense interest from researchers for over three decades, the design of scalable techniques for computing estimates with rigorous guarantees for discrete integration remains the holy grail. The key contribution of this work addresses this scalability challenge via an efficient reduction of discrete integration to model counting. The proposed reduction is achieved via a significant increase in the dimensionality that, contrary to conventional wisdom, leads to solving an instance of the relatively simpler problem of model counting. Building on the promising approach proposed by Chakraborty et al [9], our work overcomes the key weakness of their approach: a restriction to dyadic weights. We augment our proposed reduction, called DeWeight, with a state of the art efficient approximate model counter and perform detailed empirical analysis over benchmarks arising from neural network verification domains, an emerging application area of critical importance. DeWeight, to the best of our knowledge, is the first technique to compute estimates with provable guarantees for this class of benchmarks.

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Dual Hashing-Based Algorithms for Discrete Integration

Cited by 2 publications

References 13 publications

Baital: an adaptive weighted sampling approach for improved t-wise coverage

Baital: an adaptive weighted sampling approach for improved t-wise coverage

Taming Discrete Integration via the Boon of Dimensionality

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