STLts-Div: Diversified Trace Synthesis from STL Specifications Using MILP (Extended Version)
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.
Keywords
Cite
@article{arxiv.2603.17293,
title = {STLts-Div: Diversified Trace Synthesis from STL Specifications Using MILP (Extended Version)},
author = {Martin Jouve-Genty and Han Su and Sota Sato and Jie An and Zhenya Zhang and Ichiro Hasuo},
journal= {arXiv preprint arXiv:2603.17293},
year = {2026}
}