Automated generation of consistent models using qualitative abstractions and exploration strategies

• Published in: Software and systems modeling Vol. 21; no. 5; pp. 1763 - 1787
• Main Authors: Babikian, Aren A., Semeráth, Oszkár, Li, Anqi, Marussy, Kristóf, Varró, Dániel
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.10.2022; Springer Nature B.V
• Subjects: Automation; Case studies; Compilers; Computer Science; Geometric constraints; Information Systems Applications (incl.Internet); Interpreters; IT in Business; Programming Languages; Programming Techniques; Semantics; Software Engineering; Software Engineering/Programming and Operating Systems; Solvers; Space exploration; Special Section Paper; Numeric solver; Model generation; Exploration strategy; SMT-solver; Graph solver; Test scenario synthesis; Partial model
• ISSN: 1619-1366; 1619-1374; 1619-1374
• DOI: 10.1007/s10270-021-00918-6

Automatically synthesizing consistent models is a key prerequisite for many testing scenarios in autonomous driving to ensure a designated coverage of critical corner cases. An inconsistent model is irrelevant as a test case (e.g., false positive); thus, each synthetic model needs to simultaneously satisfy various structural and attribute constraints, which includes complex geometric constraints for traffic scenarios. While different logic solvers or dedicated graph solvers have recently been developed, they fail to handle either structural or attribute constraints in a scalable way. In the current paper, we combine a structural graph solver that uses partial models with an SMT-solver and a quadratic solver to automatically derive models which simultaneously fulfill structural and numeric constraints, while key theoretical properties of model generation like completeness or diversity are still ensured. This necessitates a sophisticated bidirectional interaction between different solvers which carry out consistency checks, decision, unit propagation, concretization steps. Additionally, we introduce custom exploration strategies to speed up model generation. We evaluate the scalability and diversity of our approach, as well as the influence of customizations, in the context of four complex case studies.