变量极小不可满足在模型检测中的应用

提出一个结合变量抽象和有界模型检测(BMC)的验证框架,用于证明反例不存在或输出存在反例.引入变量极小不可满足(VMU)的数学概念来驱动抽象精化的验证过程.一个VMU公式F的变量集合是保证其不可满足性的一个极小集合.严格证明了VMU驱动的精化满足抽象精化框架中的两个理想性质:有效性和极小性.虽然VMU的判定问题和极小不可满足(MU)一样难,即DP完全的,该案例研究表明,在变量抽象精化过程中,VMU比MU更为有效.

Applying Variable Minimal Unsatisfiability in Model Checking

This paper presents a framework combining variable abstraction with bounded model checking, in order to prove the counterexamples' absence or establish the counterexamples' existence. A mathematical definition of variable minimal unsatisfiability (VMU) is introduced to drive this abstraction refinement process. The set of variables of VMU formula is a minimal one guaranteeing its unsatisfiability. Furthermore, the authors prove that VMU-driven refinement is valid and minimal by mathematical reasoning. Although the determining problem of VMU is as hard as the well-known problem called minimal unsatisfiability (MU), i.e. D^P-complete, the case study has shown that VMU could be more effective than MU in variable abstraction refinement process.