论文标题
通过模型引导近似采样SMT采样
SMT Sampling via Model-Guided Approximation
论文作者
论文摘要
我们研究了令人满意的模量理论(SMT)中可令人满意的公式的领域,特别是自动生成了对此类公式的多种满足分配。尽管模型检查和正式验证中SMT的历史悠久而成功的历史,但此方面的探索却相对不足。存在用于布尔公式的此类作业或样本的先前工作,以及涉及位矢量,阵列和未解释功能的无量词的一阶公式(qf_aufbv)。我们提出了一种适合整数算术理论的新方法,并具有阵列和未解释的功能。该方法涉及将一般抽样问题减少到从一组独立间隔中采样的更简单实例,这可以有效地进行。通过使用沿原始一阶公式的约束来扩展单个模型(种子)来进行这种降低。
We investigate the domain of satisfiable formulas in satisfiability modulo theories (SMT), in particular, automatic generation of a multitude of satisfying assignments to such formulas. Despite the long and successful history of SMT in model checking and formal verification, this aspect is relatively under-explored. Prior work exists for generating such assignments, or samples, for Boolean formulas and for quantifier-free first-order formulas involving bit-vectors, arrays, and uninterpreted functions (QF_AUFBV). We propose a new approach that is suitable for a theory T of integer arithmetic and to T with arrays and uninterpreted functions. The approach involves reducing the general sampling problem to a simpler instance of sampling from a set of independent intervals, which can be done efficiently. Such reduction is carried out by expanding a single model - a seed - using top-down propagation of constraints along the original first-order formula.