SMT solvers are at the basis of many applications, such as program verification, program synthesis, and test case generation. For all these applications to provide reliable results, SMT solvers must answer queries correctly. However, since they are complex, highly-optimized software systems, ensuring their correctness is challenging. In particular, state-of-the-art testing techniques do not reliably detect when an SMT solver is unsound.
In this paper, we present an automatic approach for generating test cases that reveal soundness errors in the implementations of string solvers, as well as potential completeness and performance issues. We synthesize input formulas that are satisfiable or unsatisfiable by construction and use this ground truth as test oracle. We automatically apply satisfiability-preserving transformations to generate increasingly-complex formulas, which allows us to detect many errors with simple inputs and, thus, facilitates debugging.
The experimental evaluation shows that our technique effectively reveals bugs in the implementation of widely-used SMT solvers and applies also to other types of solvers, such as automata-based solvers. We focus on strings here, but our approach carries over to other theories and their combinations.
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Rule-based Code Generation in Industrial Automation: Four Large-scale Case Studies applying the CAYENNE MethodSEIP
Heiko KoziolekABB Corporate Research, Andreas BurgerABB Corporate Research, Marie Platenius-MohrABB Corporate Research, Julius RückertABB Corporate Research, Hadil AbukwaikABB Corporate Research, Raoul JetleyABB, Abdulla PPABB Corporate ResearchPre-print
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