SAIV 2024

Presentation
Paper

Chronosymbolic Learning: Efficient CHC Solving with Symbolic Reasoning and Inductive Learning

Ziyan Luo, Xujie Si

on  Mon, 10:00in  Main Roomfor  30min

Abstract

Solving Constrained Horn Clauses (CHCs) is a fundamental challenge behind a wide range of verification and analysis tasks. To enhance CHC solving without the laborious task of manual heuristic creation and tuning, data-driven approaches demonstrate significant potential by extracting crucial patterns from a small set of data points. However, at present, symbolic methods generally surpass data-driven solvers in performance. In this work, we develop a simple but effective framework, Chronosymbolic Learning, which unifies symbolic information and numerical data to solve a CHC system efficiently. We also present a simple instance of Chronosymbolic Learning with a data-driven learner and a BMC-styled reasoner. Despite its relative simplicity, experimental results show the efficacy and robustness of our tool. It outperforms state-of-the-art CHC solvers on a test suite of 288 arithmetic benchmarks, including some instances with non-linear arithmetic.

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