Dataset Open Access

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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        "affiliation": "Delft University of Technology", 
        "type": "Researcher", 
        "name": "Annibale Panichella"
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    "title": "Replication package of \"Search-based Crash Reproduction using Behavioral Model Seeding\"", 
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        "title": "Software Testing AMPlification", 
        "acronym": "STAMP", 
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      "https://arxiv.org/abs/1912.04606", 
      "https://github.com/STAMP-project/Botsing-model-seeding-application"
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    "keywords": [
      "seed learning", 
      "crash reproduction", 
      "search-based software testing"
    ], 
    "publication_date": "2019-10-18", 
    "creators": [
      {
        "orcid": "0000-0003-3549-9019", 
        "affiliation": "Delft University of Technology", 
        "name": "Pouria Derakhshanfar"
      }, 
      {
        "orcid": "0000-0002-0831-7606", 
        "affiliation": "Delft University of Technology", 
        "name": "Xavier Devroey"
      }, 
      {
        "orcid": "0000-0002-8431-0377", 
        "affiliation": "University of Namur", 
        "name": "Gilles Perrouin"
      }, 
      {
        "orcid": "0000-0003-2413-3935", 
        "affiliation": "Delft University of Technology", 
        "name": "Andy Zaidman"
      }, 
      {
        "orcid": "0000-0003-4850-3312", 
        "affiliation": "Delft University of Technology", 
        "name": "Arie van Deursen"
      }
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    "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>"
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