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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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  <identifier identifierType="DOI">10.5281/zenodo.3673916</identifier>
  <creators>
    <creator>
      <creatorName>Pouria Derakhshanfar</creatorName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0003-3549-9019</nameIdentifier>
      <affiliation>Delft University of Technology</affiliation>
    </creator>
    <creator>
      <creatorName>Xavier Devroey</creatorName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0002-0831-7606</nameIdentifier>
      <affiliation>Delft University of Technology</affiliation>
    </creator>
    <creator>
      <creatorName>Gilles Perrouin</creatorName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0002-8431-0377</nameIdentifier>
      <affiliation>University of Namur</affiliation>
    </creator>
    <creator>
      <creatorName>Andy Zaidman</creatorName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0003-2413-3935</nameIdentifier>
      <affiliation>Delft University of Technology</affiliation>
    </creator>
    <creator>
      <creatorName>Arie van Deursen</creatorName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0003-4850-3312</nameIdentifier>
      <affiliation>Delft University of Technology</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Replication package of "Search-based Crash Reproduction using Behavioral Model Seeding"</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2019</publicationYear>
  <subjects>
    <subject>seed learning</subject>
    <subject>crash reproduction</subject>
    <subject>search-based software testing</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2019-10-18</date>
  </dates>
  <language>en</language>
  <resourceType resourceTypeGeneral="Dataset"/>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/3673916</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.3673915</relatedIdentifier>
  </relatedIdentifiers>
  <version>1.0</version>
  <rightsList>
    <rights rightsURI="https://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&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;</description>
    <description descriptionType="Other">{"references": ["https://arxiv.org/abs/1912.04606", "https://github.com/STAMP-project/Botsing-model-seeding-application"]}</description>
  </descriptions>
  <fundingReferences>
    <fundingReference>
      <funderName>European Commission</funderName>
      <funderIdentifier funderIdentifierType="Crossref Funder ID">10.13039/501100000780</funderIdentifier>
      <awardNumber awardURI="info:eu-repo/grantAgreement/EC/H2020/731529/">731529</awardNumber>
      <awardTitle>Software Testing AMPlification</awardTitle>
    </fundingReference>
  </fundingReferences>
</resource>
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