Constraint Caching Revisited

Taljaard, Jan; Geldenhuys, Jaco; Visser, Willem

doi:10.1007/978-3-030-55754-6_15

Cited by 3 publications

(1 citation statement)

References 15 publications

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“…Similarly, caching solvers [35] for Satisfiability Modulo Theories cache obtained results or even the full models used to obtain the solution, with assignments for all variables, allowing for faster verification of subsequent queries. For program analysis tasks that deal with repeated similar inputs (e.g.…”

Section: Related Workmentioning

confidence: 99%

Shared Certificates for Neural Network Verification

Sprecher

Fischer²,

Dimitrov

et al. 2022

Lecture Notes in Computer Science

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Existing neural network verifiers compute a proof that each input is handled correctly under a given perturbation by propagating a symbolic abstraction of reachable values at each layer. This process is repeated from scratch independently for each input (e.g., image) and perturbation (e.g., rotation), leading to an expensive overall proof effort when handling an entire dataset. In this work, we introduce a new method for reducing this verification cost without losing precision based on a key insight that abstractions obtained at intermediate layers for different inputs and perturbations can overlap or contain each other. Leveraging our insight, we introduce the general concept of shared certificates, enabling proof effort reuse across multiple inputs to reduce overall verification costs. We perform an extensive experimental evaluation to demonstrate the effectiveness of shared certificates in reducing the verification cost on a range of datasets and attack specifications on image classifiers including the popular patch and geometric perturbations. We release our implementation at https://github.com/eth-sri/proof-sharing.

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Section: Related Workmentioning

confidence: 99%

Shared Certificates for Neural Network Verification

Sprecher

Fischer²,

Dimitrov

et al. 2022

Lecture Notes in Computer Science

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Triereme: Speeding up hybrid fuzzing through efficient query scheduling

Geretto,

Hohnerlein,

Giuffrida

et al. 2023

Annual Computer Security Applications Conference

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Partial Solution Based Constraint Solving Cache in Symbolic Execution

Shuai,

Chen,

et al. 2024

Proc. ACM Softw. Eng.

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Constraint solving is one of the main challenges for symbolic execution. Caching is an effective mechanism to reduce the number of the solver invocations in symbolic execution and is adopted by many mainstream symbolic execution engines. However, caching can not perform well on all programs. How to improve caching’s effectiveness is challenging in general. In this work, we propose a partial solution-based caching method for improving caching’s effectiveness. Our key idea is to utilize the partial solutions inside the constraint solving to generate more cache entries. A partial solution may satisfy other constraints of symbolic execution. Hence, our partial solution-based caching method naturally improves the rate of cache hits. We have implemented our method on two mainstream symbolic executors (KLEE and Symbolic PathFinder) and two SMT solvers (STP and Z3). The results of extensive experiments on real-world benchmarks demonstrate that our method effectively increases the number of the explored paths in symbolic execution. Our caching method achieves 1.07x to 2.3x speedups for exploring the same amount of paths on different benchmarks.

show abstract

Constraint Caching Revisited

Cited by 3 publications

References 15 publications

Shared Certificates for Neural Network Verification

Shared Certificates for Neural Network Verification

Triereme: Speeding up hybrid fuzzing through efficient query scheduling

Partial Solution Based Constraint Solving Cache in Symbolic Execution

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