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Replication package of "Search-based Crash Reproduction using Behavioral Model Seeding"

Pouria Derakhshanfar; Xavier Devroey; Gilles Perrouin; Andy Zaidman; Arie van Deursen


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    <dct:title>Replication package of "Search-based Crash Reproduction using Behavioral Model Seeding"</dct:title>
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    <dcat:keyword>seed learning</dcat:keyword>
    <dcat:keyword>crash reproduction</dcat:keyword>
    <dcat:keyword>search-based software testing</dcat:keyword>
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    <dct:description>&lt;p&gt;Search-based crash reproduction approaches assist developers during debugging by generating a test case which reproduces a crash given its stack trace. One of the fundamental steps of this approach is creating objects needed to trigger the crash. One way to overcome this limitation is seeding: using information about the application during the search process. With seeding, the existing usages of classes can be used in the&lt;br&gt; search process to produce realistic sequences of method calls which create the required objects. In this study, we introduce behavioral model seeding: a new seeding method which learns class usages from both&lt;br&gt; the system under test and existing test cases. Learned usages are then synthesized in a behavioral model (state machine). Then, this model serves to guide the evolutionary process. To assess behavioral model-seeding, we evaluate it against test-seeding (the state-of-the-art technique for seeding realistic objects) and no-seeding (without seeding any class usage). For this evaluation, we use a benchmark of 122 hard-to-reproduce crashes stemming from six open-source projects. Our results indicate that behavioral model-seeding outperforms both test seeding and no-seeding by a minimum of 6% without any notable negative impact on efficiency.&lt;/p&gt;</dct:description>
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