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Satisfiability Modulo Theories (SMT) solvers with support for the theory of strings have recently emerged as powerful tools for reasoning about string-manipulating programs. However, due to the complex semantics of extended string functions, it is challenging to develop scalable solvers for the string constraints produced by program analysis tools. We identify several classes of simplification techniques that are critical for the efficient processing of string constraints in SMT solvers. These techniques can reduce the size and complexity of input constraints by reasoning about arithmetic entailment, multisets, and string containment relationships over input terms. We provide experimental evidence that implementing them results in significant improvements over the performance of state-of-the-art SMT solvers for extended string constraints.

High-Level Abstractions for Simplifying Extended String Constraints in SMT