We introduce TRAU, an SMT solver for an expressive constraint language, including word equations, length constraints, context-free membership queries, and transducer constraints. The satisfiability problem for such a class of constraints is in general undecidable. The key idea behind TRAU is a technique called flattening, which searches for satisfying assignments that follow simple patterns. TRAU implements a Counter-Example Guided Abstraction Refinement (CEGAR) framework which contains both an under- and an over-approximation module. The approximations are refined in an automatic manner by information flow between the two modules. The technique implemented by TRAU can handle a rich class of string constraints and has better performance than state-of-the-art string solvers.

We describe a uniform and efficient framework for checking the satisfiability of a large class of string constraints. The framework is based on the observation that both satisfiability and unsatisfiability of common constraints can be demonstrated through witnesses with simple patterns. These patterns are captured using flat automata each of which consists of a sequence of simple loops. We build a Counter-Example Guided Abstraction Refinement (CEGAR) framework which contains both an under-and an over-approximation module. The flow of information between the modules allows to increase the precision in an automatic manner. We have implemented the framework as a tool and performed extensive experimentation that demonstrates both the generality and efficiency of our method.