VeriAbs: A Tool for Scalable Verification by Abstraction (Competition Contribution)

Darke, Priyanka; Agrawal, Sakshi; Venkatesh, R.

doi:10.1007/978-3-030-72013-1_32

Cited by 18 publications

(6 citation statements)

References 11 publications

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“…Lastly, it composes the result of verifying each slice. VeriAbsL uses a sequential combination of two slicers, a slicer-analyzer [7], and a novel shallow slicer or Sslicer. Sslicer is applied to programs that could not be sliced by the slicer-analyzer.…”

Section: Verification Approachmentioning

confidence: 99%

“…1 shows the architecture of VeriAbsL. As shown VeriAbsL uses the portfolio of a strategy selection-based verifier called VeriAbs [7]. Despite the advantages of sequencing multiple verification techniques in a strategy, experimental evidence indicates that each strategy works well for only a class of programs.…”

Section: Verification Approachmentioning

confidence: 99%

“…Sslicer is a generalization of the slicer-analyzer presented in [7] and like the latter, aims for a scalable slicing with respect to calls in entry function main. But unlike the slicer-analyzer, Sslicer allows multiple calls in main to (1) refer to the same global variable, (2) transitively invoke the same function, or (3) have transitive dependence on the same data element or control structure in main.…”

Section: Shallow Slicermentioning

confidence: 99%

“…It uses TCS's program analysis framework [10] for static analysis, and TensorFlow libraries [2] for learning. VeriAbsL uses Ve-riAbs's portfolio [7], except Vajra [6] because it is not supported on Ubuntu 22.04 LTS. VeriAbsL participated in the Reach-Safety category at sv-comp 2023, and is available at [3].…”

Section: Software Project Architecture and Setupmentioning

confidence: 99%

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VeriAbsL: Scalable Verification by Abstraction and Strategy Prediction (Competition Contribution)

Darke

Chimdyalwar

Agrawal

et al. 2023

Lecture Notes in Computer Science

Self Cite

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We present VeriAbsL, a reachability verifier that performs verification in three stages. First, it slices the input code using a combination of two slicers, then it verifies the slices using predicted strategies, and at last, it composes the result of verifying the individual slices. We introduce a novel shallow slicing technique that uses variable reference information of the program, and data and control dependencies of the entry function to generate slices. We also introduce a novel strategy prediction technique that uses machine learning to predict a strategy. It uses boolean features to describe a program to a neural network that predicts a strategy. We use the portfolio of VeriAbs, a reachabiltiy verifier with manually defined strategies. In sv-comp 2023, VeriAbsL verified 227 (Without witness validation.) more programs than VeriAbs, and 475 (Without witness validation.) programs that VeriAbs could not verify.

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Section: Verification Approachmentioning

confidence: 99%

Section: Verification Approachmentioning

confidence: 99%

Section: Shallow Slicermentioning

confidence: 99%

Section: Software Project Architecture and Setupmentioning

confidence: 99%

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VeriAbsL: Scalable Verification by Abstraction and Strategy Prediction (Competition Contribution)

Darke

Chimdyalwar

Agrawal

et al. 2023

Lecture Notes in Computer Science

Self Cite

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“…This has motivated the creation of algorithm selectors using suites of tools from the competition. In fact, there are tools which compete using algorithm selectors [17,52].…”

Section: Automated Software Verification Toolsmentioning

confidence: 99%

Algorithm Selection for Software Verification using Graph Attention Networks

Leeson¹,

B²

2022

Preprint

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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 11%. We conjecture this is in part due to Graves' use of GNNs with attention mechanisms. Through a qualitative study on model interpretability, we find strong evidence that the Graves' GNN-based model learns to base its predictions on factors that relate to the unique features of the algorithmic techniques. CCS Concepts: • Software and its engineering → Formal software verification; • Computing methodologies → Neural networks; • Mathematics of computing → Graph algorithms.

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Software Verification: 10th Comparative Evaluation (SV-COMP 2021)

Beyer

2021

Tools and Algorithms for the Construction and Analysis of Systems

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SV-COMP 2021 is the 10th edition of the Competition on Software Verification (SV-COMP), which is an annual comparative evaluation of fully automatic software verifiers for C and Java programs. The competition provides a snapshot of the current state of the art in the area, and has a strong focus on reproducibility of its results. The competition was based on 15 201 verification tasks for C programs and 473 verification tasks for Java programs. Each verification task consisted of a program and a property (reachability, memory safety, overflows, termination). SV-COMP 2021 had 30 participating verification systems from 27 teams from 11 countries.

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VeriAbs: A Tool for Scalable Verification by Abstraction (Competition Contribution)

Cited by 18 publications

References 11 publications

VeriAbsL: Scalable Verification by Abstraction and Strategy Prediction (Competition Contribution)

VeriAbsL: Scalable Verification by Abstraction and Strategy Prediction (Competition Contribution)

Algorithm Selection for Software Verification using Graph Attention Networks

Software Verification: 10th Comparative Evaluation (SV-COMP 2021)

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