Chronosymbolic Learning: Efficient CHC Solving with Symbolic Reasoning and Inductive Learning
This addresses the fundamental challenge of CHC solving for verification and analysis tasks, offering an incremental improvement by combining existing methods.
The authors tackled the performance gap between data-driven and symbolic reasoning-based solvers for Constrained Horn Clauses (CHCs) by developing Chronosymbolic Learning, a framework that unifies symbolic and numerical data, resulting in a tool that outperforms state-of-the-art solvers on 288 benchmarks, including non-linear integer arithmetic instances.
Solving Constrained Horn Clauses (CHCs) is a fundamental challenge behind a wide range of verification and analysis tasks. Data-driven approaches show great promise in improving CHC solving without the painstaking manual effort of creating and tuning various heuristics. However, a large performance gap exists between data-driven CHC solvers and symbolic reasoning-based solvers. In this work, we develop a simple but effective framework, "Chronosymbolic Learning", which unifies symbolic information and numerical data points 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 dataset consisting of 288 benchmarks, including many instances with non-linear integer arithmetics.