arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2109.03330cs.LOcs.SEcs.SYeess.SY
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基于仿真的形式化验证中约束场景的任意水平均匀随机抽样和枚举

Any-horizon uniform random sampling and enumeration of constrained scenarios for simulation-based formal verification

Toni Mancini, Igor Melatti, Enrico Tronci

摘要:

基于模型的方法来验证的非终止的信息物理系统(CPS)通常依赖于数值模拟的系统下验证(SUV)模型的输入方案可能不同的持续时间,选择那些满足给定的约束条件。这种约束通常源于对SUV输入及其操作环境的要求(或假设),以及旨在实现以下目标的附加条件的实施,例如,对(通常非常长的)验证活动进行优先级排序,例如,专注于明确执行选定需求的场景,或避免满足需求的空洞。在这种情况下,有效地随机采样的可能性(具有已知的分布,例如,均匀地)在满足给定约束的那些场景中,或者在满足给定约束的那些场景中有效地枚举(可能以均匀随机的顺序)场景是验证过程的可行性的关键使能器,例如,通过基于模拟的统计模型检验。不幸的是,在约束的非平凡组合的情况下,像输入序列空间中的马尔可夫随机游走这样的迭代方法通常无法根据给定的分布提取场景,并且对于产生感兴趣的法律场景可能非常低效。我们展示了如何,给定一组的限制输入方案简洁地定义有限内存监视器,一个数据结构(场景生成器)可以合成,从任何地平线的情况下,满足输入约束可以有效地提取(可能是均匀的)随机采样或(随机)枚举。我们的方法能够无缝支持几乎所有基于模拟的CPS验证方法,从简单的随机测试到统计模型检验和正式(即,详尽的)验证。

英文摘要:

Model-based approaches to the verification of non-terminating Cyber-Physical Systems (CPSs) usually rely on numerical simulation of the System Under Verification (SUV) model under input scenarios of possibly varying duration, chosen among those satisfying given constraints. Such constraints typically stem from requirements (or assumptions) on the SUV inputs and its operational environment as well as from the enforcement of additional conditions aiming at, e.g., prioritising the (often extremely long) verification activity, by, e.g., focusing on scenarios explicitly exercising selected requirements, or avoiding vacuity in their satisfaction. In this setting, the possibility to efficiently sample at random (with a known distribution, e.g., uniformly) within, or to efficiently enumerate (possibly in a uniformly random order) scenarios among those satisfying the given constraints is a key enabler for the viability of the verification process, e.g., via simulation-based statistical model checking. Unfortunately, in case of non-trivial combinations of constraints, iterative approaches like Markovian random walks in the space of sequences of inputs in general fail in extracting scenarios according to a given distribution, and can be very inefficient to produce legal scenarios of interest. We show how, given a set of constraints on the input scenarios succinctly defined by finite memory monitors, a data structure (scenario generator) can be synthesised, from which any-horizon scenarios satisfying the input constraints can be efficiently extracted by (possibly uniform) random sampling or (randomised) enumeration. Our approach enables seamless support to virtually all simulation-based approaches to CPS verification, ranging from simple random testing to statistical model checking and formal (i.e., exhaustive) verification.

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