Ultimate Taipan with Dynamic Block Encoding

Dietsch, Daniel; Greitschus, Marius; Heizmann, Matthias; Hoenicke, Jochen; Nutz, Alexander; Podelski, Andreas; Schilling, Christian; Schindler, Tanja

doi:10.1007/978-3-319-89963-3_31

Cited by 3 publications

(1 citation statement)

References 11 publications

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“…Within this sub-category, we selected the benchmarks in the recursive sub-directory that contained true assertions, yielding a set of 17 benchmarks. We ran CHORA, ICRA, and the top three performers on this category from the 2019 competition: Ultimate Automizer (UA) [16], UTaipan [13], and VIAP [28]. Fig.…”

Section: Benchmark Actualmentioning

confidence: 99%

Templates and recurrences: better together

Breck

Cyphert

Kincaid

et al. 2020

Proceedings of the 41st ACM SIGPLAN Conference on Programming Language Design and Implementation

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This paper is the confluence of two streams of ideas in the literature on generating numerical invariants, namely: (1) template-based methods, and (2) recurrence-based methods.A template-based method begins with a template that contains unknown quantities, and finds invariants that match the template by extracting and solving constraints on the unknowns. A disadvantage of template-based methods is that they require fixing the set of terms that may appear in an invariant in advance. This disadvantage is particularly prominent for non-linear invariant generation, because the user must supply maximum degrees on polynomials, bases for exponents, etc.On the other hand, recurrence-based methods are able to find sophisticated non-linear mathematical relations, including polynomials, exponentials, and logarithms, because such relations arise as the solutions to recurrences. However, a disadvantage of past recurrence-based invariant-generation methods is that they are primarily loop-based analyses: they use recurrences to relate the pre-state and post-state of a loop, so it is not obvious how to apply them to a recursive procedure, especially if the procedure is non-linearly recursive (e.g., a tree-traversal algorithm).In this paper, we combine these two approaches and obtain a technique that uses templates in which the unknowns are functions rather than numbers, and the constraints on the unknowns are recurrences. The technique synthesizes invariants involving polynomials, exponentials, and logarithms, even in the presence of arbitrary control-flow, including any

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Section: Benchmark Actualmentioning

confidence: 99%

Templates and recurrences: better together

Breck

Cyphert

Kincaid

et al. 2020

Proceedings of the 41st ACM SIGPLAN Conference on Programming Language Design and Implementation

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Separating Map Variables in a Logic-Based Intermediate Verification Language

Dietsch¹,

Heizmann²,

Hoenicke³

et al. 2021

Networked Systems

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Algorithm Selection for Software Verification Using Graph Neural Networks

Leeson,

Dwyer

2024

ACM Trans. Softw. Eng. Methodol.

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The field of software verification has produced a wide array of algorithmic techniques that can prove a variety of properties of a given program. It has been demonstrated that the performance of these techniques can vary up to 4 orders of magnitude on the same verification problem. Even for verification experts, it is difficult to decide which tool will perform best on a given problem. For general users, deciding the best tool for their verification problem is effectively impossible. In this work, we present Graves , a selection strategy based on graph neural networks (GNNs). Graves generates a graph representation of a program from which a GNN predicts a score for a verifier that indicates its performance on the program. We evaluate Graves on a set of 10 verification tools and over 8000 verification problems and find that it improves the state-of-the-art in verification algorithm selection by 12%, or 8 percentage points. Further, it is able to verify 9% more problems than any existing verifier on our test set. Through a qualitative study on model interpretability, we find strong evidence that the Graves ’ model learns to base its predictions on factors that relate to the unique features of the algorithmic techniques.

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Ultimate Taipan with Dynamic Block Encoding

Cited by 3 publications

References 11 publications

Templates and recurrences: better together

Templates and recurrences: better together

Separating Map Variables in a Logic-Based Intermediate Verification Language

Algorithm Selection for Software Verification Using Graph Neural Networks

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