Abstract
Advanced tools used in model-based systems engineering (MBSE) frequently represent their models as graphs. In order to test those tools, the automated generation of well-formed (or intentionally malformed) graph models is necessitated which is often carried out by solver-based model generation techniques. In many model generation scenarios, one needs more refined control over the generated unit tests to focus on the more relevant models. Type scopes allow to precisely define the required number of newly generated elements, thus one can avoid the generation of unrealistic and highly symmetric models having only a single type of elements. In this paper, we propose a 3-valued scoped partial modeling formalism, which innovatively extends partial graph models with predicate abstraction and counter abstraction. As a result, well-formedness constraints and multiplicity requirements can be evaluated in an approximated way on incomplete (unfinished) models by using advanced graph query engines with numerical solvers (e.g., IP or LP solvers). Based on the refinement of 3-valued scoped partial models, we propose an efficient model generation algorithm that generates models that are both well-formed and satisfy the scope requirements. We show that the proposed approach scales significantly better than existing SAT-solver techniques or the original graph solver without multiplicity reasoning. We illustrate our approach in a complex design-space exploration case study of collaborating satellites introduced by researchers at NASA JPL.
Highlights
M ODEL-based systems engineering frequently uses complex modeling tools, like Capella, Artop, Matlab Simulink or Yakindu Statecharts
Since our current work focuses on model generation for the structural part of graph models, we omit the detailed handling of attributes, which could be introduced
We propose a new 3-valued scoped partial modeling formalism which allows to explicitly represent multiplicity constraints on the size of partial models with a linear inequality system
Summary
M ODEL-based systems engineering frequently uses complex modeling tools, like Capella, Artop, Matlab Simulink or Yakindu Statecharts. Several generators are based on precise foundations offered by backend logic solvers (like SAT solvers [22], [23] or SMT solvers [24]) These tools excel at finding inconsistencies (if they exist) by interpreting domain specifications as a logic problem, but they can only derive small consistent models. They fail to derive a diverse set of models [20], [25], which restricts their use in practical testing scenarios. This significantly improves the performance of existing graph solver algorithms It enables a practical iterative workflow for test generation where initially, one can start with general scopes which are gradually refined to grow larger consistent models. By adhering to the refinement calculus, the generator continues to provide favorable properties such as consistency, completeness or diversity (but the in-depth investigation of such properties is out of scope for the paper)
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