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arXiv 2201.11711cs.SE
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使用图神经网络进行软件验证的算法选择

Algorithm Selection for Software Verification using Graph Neural Networks

Will Leeson, Matthew B Dwyer

AI总结:

本文提出Graves,一种基于图神经网络的软件验证算法选择策略,通过程序图表示预测验证器性能,在10个工具和8000多个问题上比现有最先进方法提升12%,并能多验证9%的问题。

摘要:

软件验证领域已经产生了大量的算法技术,这些技术能够证明给定程序的多种属性。已有研究表明,这些技术在同一验证问题上的性能差异可高达4个数量级。即使是验证专家,也难以决定哪个工具在给定问题上表现最佳。对于普通用户而言,为其验证问题选择最佳工具实际上是不可能的。在这项工作中,我们提出了Graves,一种基于图神经网络(GNNs)的选择策略。Graves生成程序的图表示,GNN据此为验证器预测一个分数,该分数指示验证器在该程序上的性能。我们在10个验证工具和超过8000个验证问题上评估了Graves,发现它比现有最先进的验证算法选择方法提高了12%,即8个百分点。此外,在我们的测试集上,它能够比任何现有验证器多验证9%的问题。通过对模型可解释性的定性研究,我们找到了强有力的证据表明Graves模型学习基于与算法技术独特特征相关的因素来进行预测。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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