Towards the Automated Generation of Consistent, Diverse, Scalable and Realistic Graph Models

• Published in: Graph Transformation, Specifications, and Nets Vol. 10800; pp. 285 - 312
• Main Authors: Varró, Dániel, Semeráth, Oszkár, Szárnyas, Gábor, Horváth, Ákos
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 2018; Springer International Publishing
• Series: Lecture Notes in Computer Science
• Subjects: Automated model generation; Partial models; Refinement
• ISBN: 9783319753959; 3319753959
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-319-75396-6_16

Automated model generation can be highly beneficial for various application scenarios including software tool certification, validation of cyber-physical systems or benchmarking graph databases to avoid tedious manual synthesis of models. In the paper, we present a long-term research challenge how to generate graph models specific to a domain which are consistent, diverse, scalable and realistic at the same time. We provide foundations for a class of model generators along a refinement relation which operates over partial models with 3-valued representation and ensures that subsequently derived partial models preserve the truth evaluation of well-formedness constraints in the domain. We formally prove completeness, i.e. any finite instance model of a domain can be generated by model generator transformations in finite steps and soundness, i.e. any instance model retrieved as a solution satisfies all well-formedness constraints. An experimental evaluation is carried out in the context of a statechart modeling tool to evaluate the trade-off between different characteristics of model generators.