Towards Comprehensive Sampling of SMT Solutions

TL;DR

This paper introduces PanSampler, a novel SMT sampling method that efficiently generates diverse solutions to improve coverage and fault detection in software and hardware testing, reducing testing costs.

Contribution

PanSampler employs a diversity-aware algorithm, AST-guided scoring, and local search to produce high-coverage solutions with fewer samples, advancing SMT sampling techniques.

Findings

Achieves higher coverage with fewer solutions than existing methods.

Reduces test case requirements by up to 76.4% for fault detection.

Enhances testing efficiency in real-world software systems.

Abstract

This work focuses on effectively generating diverse solutions for satisfiability modulo theories (SMT) formulas, targeting the theories of bit-vectors, arrays, and uninterpreted functions, which is a critical task in software and hardware testing. Generating diverse SMT solutions helps uncover faults and detect safety violations during the verification and testing process, resulting in the SMT sampling problem, i.e., constructing a small number of solutions while achieving comprehensive coverage of the constraint space. While high coverage is crucial for exploring system behaviors, reducing the number of solutions is of great importance, as excessive solutions increase testing time and resource usage, undermining efficiency. In this work, we introduce PanSampler, a novel SMT sampler that achieves high coverage with a small number of solutions. It incorporates three novel techniques, i.e., diversity-aware SMT algorithm, abstract syntax tree (AST)-guided scoring function and post-sampling optimization technology, enhancing its practical performance. It iteratively samples solutions, evaluates candidates, and employs local search to refine solutions, ensuring high coverage with a small number of samples. Extensive experiments on practical benchmarks demonstrate that PanSampler exhibits a significantly stronger capability to reach high target coverage, while requiring fewer solutions than current samplers to achieve the same coverage level. Furthermore, our empirical evaluation on practical subjects, which are collected from real-world software systems, shows that PanSampler achieves higher fault detection capability and reduces the number of required test cases from 32.6\% to 76.4\% to reach the same fault detection effectiveness, leading to a substantial improvement in testing efficiency. PanSampler advances SMT sampling, reducing the cost of software testing and hardware verification.