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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  "publisher": "Zenodo", 
  "DOI": "10.5281/zenodo.3673916", 
  "language": "eng", 
  "title": "Replication package of \"Search-based Crash Reproduction using Behavioral Model Seeding\"", 
  "issued": {
    "date-parts": [
  "abstract": "<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>", 
  "author": [
      "family": "Pouria Derakhshanfar"
      "family": "Xavier Devroey"
      "family": "Gilles Perrouin"
      "family": "Andy Zaidman"
      "family": "Arie van Deursen"
  "version": "1.0", 
  "type": "dataset", 
  "id": "3673916"
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