Learning GR(1) Specifications from Traces

2026-08-06

AI Summary

This work addresses the problem of automatically mining synthesizable GR(1) specifications from system execution traces by introducing GR1MINE, an efficient and specialized mining framework. GR1MINE integrates GR(1) temporal skeleton analysis, incremental formula enumeration, and conflict-driven clause learning, leveraging SAT solving to prune redundant search paths and substantially improve both efficiency and coverage of mined specifications. As the first dedicated framework for the GR(1) fragment, GR1MINE achieves a speedup of over 30× compared to general-purpose LTL mining tools on the Syntech benchmark suite and recovers more than twice as many realizable specifications on non-GR(1) instances from SYNTCOMP.

Abstract

Constrained specification mining enables the automatic discovery of desired properties from system traces. Generalized Reactivity of Rank 1, or GR(1), is a fragment of LTL with polynomial-time synthesis that natively encodes assume-guarantee properties present in most hardware and robotics domains. In this paper, we present GR1MINE, a SAT-based tool for efficiently learning GR(1) formulas from examples. We exploit the GR(1) temporal skeleton to incrementally enumerate formula candidates, leveraging learnt clauses to avoid recomputation. On the Boolean GR(1) Syntech suite, GR1MINE learns a realizable formula for all 60 benchmarks over 30X faster than generic and constrained LTL mining tools. On non-GR(1) specifications from SYNTCOMP, GR1MINE is still able to recover >2X more realizable specifications than baselines within the timeout.

Problem

• GR(1)
• specification mining
• system traces
• formal specifications
• reactive synthesis

Innovation

• GR(1) specification mining
• SAT-based learning
• incremental enumeration
• temporal skeleton
• realizable specification synthesis