Stateless model checking (SMC) is one of the standard approaches to the verification of concurrent programs. As scheduling non-determinism creates exponentially large spaces of thread interleavings, SMC attempts to partition this space into equivalence classes and explore only a few representatives from each class. The efficiency of this approach depends on two factors: (a) the coarseness of the partitioning, and (b) the time to generate representatives in each class. For this reason, the search for coarse partitionings that are efficiently explorable is an active research challenge. In this work we present RVF-SMC, a new SMC algorithm that uses a novel \emph{reads-value-from (RVF)} partitioning. Intuitively, two interleavings are deemed equivalent if they agree on the value obtained in each read event, and read events induce consistent causal orderings between them. The RVF partitioning is provably coarser than recent approaches based on Mazurkiewicz and "reads-from" partitionings. Our experimental evaluation reveals that RVF is quite often a very effective equivalence, as the underlying partitioning is exponentially coarser than other approaches. Moreover, RVF-SMC generates representatives very efficiently, as the reduction in the partitioning is often met with significant speed-ups in the model checking task.

中文翻译：

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等效值下的无状态模型检查

无状态模型检查（SMC）是并发程序验证的标准方法之一。由于调度不确定性会产生大量的线程交织空间，SMC会尝试将此空间划分为等效类，并仅探索每个类中的几个代表。这种方法的效率取决于两个因素：（a）划分的粗糙程度，以及（b）在每个类别中生成代表的时间。因此，寻找可有效探索的粗略分区是一项积极的研究挑战。在这项工作中，我们介绍了RVF-SMC，这是一种使用新颖的\ emph {reads-value-from（RVF）}分区的新SMC算法。直观地讲，如果两个交错在每个读取事件中获得的值一致，则认为它们是等效的，阅读事件会在它们之间引起一致的因果顺序。事实证明，RVF分区比基于Mazurkiewicz和“ reads from”分区的最新方法要粗糙。我们的实验评估表明，RVF通常是非常有效的等效项，因为基础分区比其他方法成指数地更粗糙。此外，RVF-SMC非常有效地生成代表，因为在模型检查任务中，通常可以通过显着提高速度来满足分区减少的需求。