试验决策过程中随机模态公式的通用生成方法
A New General Method to Generate Random Modal Formulae for Testing Decision Procedures
摘要：
最近出现的高度优化的模态决策程序突出了在这一领域的实证检验的关键作用。不幸的是，引入广泛的经验测试模态逻辑是最近，到目前为止，没有一个建议的测试生成器是非常令人满意的。为了应对这一事实，我们提出了一个新的随机生成方法，提供了比以前的方法产生的经验检验的好处。它修复并推广了最著名的方法之一，随机CNF_[]m测试，允许生成更广泛的问题，原则上覆盖整个输入空间。我们的新方法产生更合适的测试集，为当前一代的模态决策程序。我们通过大量的实证检验分析了新方法的特点。
英文摘要：
The recent emergence of heavily-optimized modal decision procedures has highlighted the key role of empirical testing in this domain. Unfortunately, the introduction of extensive empirical tests for modal logics is recent, and so far none of the proposed test generators is very satisfactory. To cope with this fact, we present a new random generation method that provides benefits over previous methods for generating empirical tests. It fixes and much generalizes one of the best-known methods, the random CNF_[]m test, allowing for generating a much wider variety of problems, covering in principle the whole input space. Our new method produces much more suitable test sets for the current generation of modal decision procedures. We analyze the features of the new method by means of an extensive collection of empirical tests.
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