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Extract, model, refine: improved modelling of program verification tools through data enrichment.

Sophie Lathouwers1, Yujie Liu1, Vadim Zaytsev1

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Summary
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This study introduces a megamodel for classifying over 400 program verification tools. The curated dataset aids software engineers in selecting and comparing tools for system correctness.

Keywords:
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Area of Science:

  • Software Engineering
  • Formal Methods
  • Computer Science

Background:

  • Models are crucial in software engineering for reasoning about system correctness.
  • Program verification techniques offer varying levels of correctness guarantees.
  • A structured approach is needed to navigate the diverse landscape of verification tools.

Purpose of the Study:

  • To develop a concise megamodel for categorizing program verification tools.
  • To present a comprehensive dataset of 400+ program verification tools.
  • To facilitate tool selection, comparison, and trend identification for software engineers.

Main Methods:

  • Investigated the domain of program verification tools.
  • Developed a megamodel for tool classification.
  • Compiled a dataset with tool categories and practical information (e.g., input/output, repository links).
  • Automated data extraction and utilized APIs for data upkeep.

Main Results:

  • A megamodel distinguishing various program verification tools.
  • A publicly available dataset of over 400 tools with detailed categorizations.
  • Identification of trends within the program verification tool landscape.
  • Automated data maintenance enhancing dataset scalability.

Conclusions:

  • The megamodel and dataset provide a valuable resource for software engineers.
  • The categorization aids in finding, investigating, and comparing suitable verification tools.
  • The dataset supports easier entry into program verification and informed tool selection based on specific requirements.