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The Train Benchmark: cross-technology performance evaluation of continuous model queries.

Gábor Szárnyas1,2,3, Benedek Izsó1, István Ráth1,4

  • 11Department of Measurement and Information Systems, Budapest University of Technology and Economics, Magyar tudósok krt. 2, Budapest, 1117 Hungary.

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Summary
This summary is machine-generated.

This study introduces a benchmark for validating safety-critical system models. It assesses the performance of declarative queries for detecting design flaws in large graph models, crucial for efficient system development.

Keywords:
Graph databasesPerformance benchmarkQuery evaluationRelational databasesSemantic databasesWell-formedness validation

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Area of Science:

  • Software Engineering
  • Model-Driven Development
  • Safety-Critical Systems

Background:

  • Model-driven development (MDD) for safety-critical systems requires rigorous model validation to identify design flaws early.
  • Current industrial tools often use complex, imperative code for validation, hindering maintainability and scalability.
  • Increasing model size and complexity challenge the efficiency of existing validation tools.

Purpose of the Study:

  • To present a benchmark for systematically evaluating the scalability of well-formedness constraint validation and revalidation on large graph models.
  • To assess the performance of declarative queries and model transformations for constraint checking and repair operations.
  • To provide a standardized method for comparing different technologies and query engines in the context of model validation.

Main Methods:

  • Development of the 'Train Benchmark' featuring a railway domain metamodel, instance model generator, and well-formedness constraints.
  • Implementation of constraints as declarative queries and fault injection/repair operations as model transformations.
  • Focus on performance metrics including query evaluation time and memory consumption, with emphasis on revalidation efficiency.

Main Results:

  • The benchmark enables systematic assessment of validation and revalidation scalability for large graph models.
  • Demonstrates the applicability of the benchmark across various technologies, including modeling tools, relational, graph, and semantic databases.
  • Provides a foundation for comparing the performance of different query engines for model validation tasks.

Conclusions:

  • Declarative queries offer a more maintainable and potentially scalable approach to well-formedness validation compared to imperative code.
  • The Train Benchmark serves as a valuable open-source resource for researchers and practitioners in safety-critical system development.
  • Performance evaluation using the benchmark is crucial for selecting appropriate technologies for efficient model validation in complex systems.