Metamorphic Testing for Floating-Point Performance Issues in SMT Solvers

2026-09-17

AI Summary

该研究针对SMT求解器处理浮点公式时的性能问题，提出了一种基于语义保持重写规则的变体测试方法，并通过实际测试输入揭示了显著的性能下降现象。

Abstract

SMT solvers are essential in various domains, including program verification and synthesis. Although their correctness and performance have been extensively studied, performance testing for the floating-point theory remains limited, particularly for real-world queries. We propose a metamorphic testing approach that uses semantics-preserving rewrite rules, focusing on floating-point special values and peep-hole optimizations, to uncover meaningful performance issues in SMT solvers' handling of floating-point formulas. Using real-world test inputs, our approach is able to identify for every solver tested SMT queries for which solving time increases when the queries are simplified; we see slowdowns of up to 33.4x for Z3, 5.6x for MathSAT, 4.5x for cvc5, and 1.8x for Bitwuzla. We further observe that the approach is less successful when using input files that were randomly generated from a grammar.

Problem

• SMT solvers
• floating-point performance
• real-world queries

Innovation

• metamorphic testing
• floating-point performance
• SMT solvers
• semantics-preserving rewrite rules