Automated generation of consistent, diverse and structurally realistic graph models

• Published in: Software and systems modeling Vol. 20; no. 5; pp. 1713 - 1734
• Main Authors: Semeráth, Oszkár, Babikian, Aren A., Chen, Boqi, Li, Chuning, Marussy, Kristóf, Szárnyas, Gábor, Varró, Dániel
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.10.2021; Springer Nature B.V
• Subjects: Automation; Compilers; Computer Science; Constraint modelling; Generators; Information Systems Applications (incl.Internet); Interpreters; IT in Business; Metamodels; Programming Languages; Programming Techniques; Regular Paper; Software Engineering; Software Engineering/Programming and Operating Systems; Subject specialists; Model generation; Domain-specific languages; Graph metrics; Test generation
• ISSN: 1619-1366; 1619-1374
• DOI: 10.1007/s10270-021-00884-z

In this paper, we present a novel technique to automatically synthesize consistent, diverse and structurally realistic domain-specific graph models. A graph model is (1) consistent if it is metamodel-compliant and it satisfies the well-formedness constraints of the domain; (2) it is diverse if local neighborhoods of nodes are highly different; and (1) it is structurally realistic if a synthetic graph is at a close distance to a representative real model according to various graph metrics used in network science, databases or software engineering. Our approach grows models by model extension operators using a hill-climbing strategy in a way that (A) ensures that there are no constraint violation in the models (for consistency reasons), while (B) more realistic candidates are selected to minimize a target metric value (wrt. the representative real model). We evaluate the effectiveness of the approach for generating realistic models using multiple metrics for guidance heuristics and compared to other model generators in the context of three case studies with a large set of real human models. We also highlight that our technique is able to generate a diverse set of models, which is a requirement in many testing scenarios.