September 22, 2020 Software Open Access

Mukherjee, Suvam; Deligiannis, Pantazis; Biswas, Arpita; Lal, Akash

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    <dct:title>Learning-based Controlled Concurrency Testing</dct:title>
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    <dcat:keyword>concurrency</dcat:keyword>
    <dcat:keyword>testing</dcat:keyword>
    <dcat:keyword>model checking</dcat:keyword>
    <dcat:keyword>reinforcement learning</dcat:keyword>
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    <dct:description>&lt;p&gt;Concurrency bugs are notoriously hard to detect and reproduce. Controlled concurrency testing (CCT) techniques aim to offer a solution, where a&amp;nbsp;&lt;em&gt;scheduler&lt;/em&gt;&amp;nbsp;explores the space of possible interleavings of a concurrent program looking for bugs. Since the set of possible interleavings is typically very large, these schedulers employ heuristics that prioritize the search to &amp;quot;interesting&amp;quot;&amp;nbsp;subspaces. However, current heuristics are typically tuned to specific bug patterns, which limits their effectiveness in practice.&lt;/p&gt; &lt;p&gt;In this artifact, we present QL, a learning-based CCT framework where the likelihood of an action being selected by the scheduler is influenced by earlier explorations. We leverage the classical Q-learning algorithm to explore the space of possible interleavings, allowing the exploration to adapt to the program under test, unlike previous techniques. We have implemented and evaluated QL&amp;nbsp;on a set of microbenchmarks, complex protocols, as well as production cloud services. In our experiments, we found QL&amp;nbsp;to consistently outperform the state-of-the-art in CCT.&lt;/p&gt; &lt;p&gt;Please refer to the README file for more details on how to run the artifact.&lt;/p&gt;</dct:description>
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