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Automated Generation of Consistent Graph Models with First-Order Logic Theorem Provers

机译:具有一阶逻辑定理证明的自动生成一致图模型

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The automated generation of graph models has become an enabler in several testing scenarios, including the testing of modeling environments used in the design of critical systems, or the synthesis of test contexts for autonomous vehicles. Those approaches rely on the automated construction of consistent graph models, where each model satisfies complex structural properties of the target domain captured in first-order logic predicates. In this paper, we propose a transformation technique to map such graph generation tasks to a problem consisting of first-order logic formulae, which can be solved by state-of-the-art TPTP-compliant theorem provers, producing valid graph models as outputs. We conducted performance measurements over all 73 theorem provers available in the TPTP library, and compared our approach with other solver-based approaches like Alloy and VIATRA Solver.
机译:图模型的自动生成已成为几种测试场景的推动者,其中包括测试关键系统设计中使用的建模环境或自动驾驶汽车测试环境的综合。这些方法依赖于一致的图形模型的自动构建,其中每个模型都满足一阶逻辑谓词中捕获的目标域的复杂结构属性。在本文中,我们提出了一种转换技术,可以将此类图生成任务映射到由一阶逻辑公式组成的问题,可以通过最新的TPTP兼容定理证明来解决,从而生成有效的图模型作为输出。我们对TPTP库中提供的所有73个定理证明者进行了性能测量,并将我们的方法与其他基于求解器的方法(例如Alloy和VIATRA Solver)进行了比较。

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