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STLts-Div: Diversified Trace Synthesis from STL Specifications Using MILP (Extended Version)

Year: 2026

Type: preprint

Abstract: Modern cyber-physical systems are complex, and requirements are often written in Signal Temporal Logic (STL). Writing the right STL is difficult in practice; engineers benefit from concrete executions that illustrate what a specification actually admits. Trace synthesis addresses this need, but a single witness rarely suffices to understand intent or explore edge cases - diverse satisfying behaviors are far more informative. We introduce diversified trace synthesis: the automatic generation of sets of behaviorally diverse traces that satisfy a given STL formula. Building on a MILP encoding of STL and system model, we formalize three complementary diversification objectives - Boolean distance, random Boolean distance, and value distance - all captured by an objective function and solved iteratively. We implement these ideas in STLts-Div, a lightweight Python tool that integrates with Gurobi.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2603.17293

Authors: Jouve-Genty, Martin, Su, Han, Sato, Sota, An, Jie, Zhang, Zhenya, Hasuo, Ichiro

Institutions: École Normale Supérieure de Lyon, National Institute of Informatics, Laboratoire de Physique de l'ENS de Lyon, Laboratoire de l'Informatique du Parallélisme, The Graduate University for Advanced Studies, SOKENDAI, Chinese Academy of Sciences, Institute of Software, Kyushu University

Topic: Formal Methods in Verification

Subfield: Computational Theory and Mathematics

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funder: Japan Society for the Promotion of Science

OpenAlex ID: W7138996451

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