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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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{
  "inLanguage": {
    "alternateName": "eng", 
    "@type": "Language", 
    "name": "English"
  }, 
  "description": "<p>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<br>\nsearch 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<br>\nthe 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.</p>", 
  "license": "https://creativecommons.org/licenses/by/4.0/legalcode", 
  "creator": [
    {
      "affiliation": "Delft University of Technology", 
      "@id": "https://orcid.org/0000-0003-3549-9019", 
      "@type": "Person", 
      "name": "Pouria Derakhshanfar"
    }, 
    {
      "affiliation": "Delft University of Technology", 
      "@id": "https://orcid.org/0000-0002-0831-7606", 
      "@type": "Person", 
      "name": "Xavier Devroey"
    }, 
    {
      "affiliation": "University of Namur", 
      "@id": "https://orcid.org/0000-0002-8431-0377", 
      "@type": "Person", 
      "name": "Gilles Perrouin"
    }, 
    {
      "affiliation": "Delft University of Technology", 
      "@id": "https://orcid.org/0000-0003-2413-3935", 
      "@type": "Person", 
      "name": "Andy Zaidman"
    }, 
    {
      "affiliation": "Delft University of Technology", 
      "@id": "https://orcid.org/0000-0003-4850-3312", 
      "@type": "Person", 
      "name": "Arie van Deursen"
    }
  ], 
  "url": "https://zenodo.org/record/3673916", 
  "datePublished": "2019-10-18", 
  "keywords": [
    "seed learning", 
    "crash reproduction", 
    "search-based software testing"
  ], 
  "version": "1.0", 
  "contributor": [
    {
      "affiliation": "Delft University of Technology", 
      "@id": "https://orcid.org/0000-0002-7395-3588", 
      "@type": "Person", 
      "name": "Annibale Panichella"
    }
  ], 
  "@context": "https://schema.org/", 
  "distribution": [
    {
      "contentUrl": "https://zenodo.org/api/files/abdd7f6a-b89b-4795-9e83-95ccf13f9041/Botsing-model-seeding-application-master.zip", 
      "encodingFormat": "zip", 
      "@type": "DataDownload"
    }
  ], 
  "identifier": "https://doi.org/10.5281/zenodo.3673916", 
  "@id": "https://doi.org/10.5281/zenodo.3673916", 
  "@type": "Dataset", 
  "name": "Replication package of \"Search-based Crash Reproduction using Behavioral Model Seeding\""
}
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