Model generation for quantified formulas with application to test data generation

Abstract

We present a new model generation approach and technique for solving first-order logic (FOL) formulas with quantifiers in unbounded domains. Model generation is important, e.g., for test data generation based on test data constraints and for counterexample generation in formal verification. In such scenarios, quantified FOL formulas have to be solved stemming, e.g., from formal specifications. Satisfiability modulo theories (SMT) solvers are considered as the state-of-the-art techniques for generating models of FOL formulas. Handling of quantified formulas in the combination of theories is, however, sometimes a problem. Our approach addresses this problem and can solve formulas that were not solvable before using SMT solvers. We present the model generation algorithm and show how to convert a representation of a model into a test preamble for state initialization with test data. A prototype of this algorithm is implemented in the formal verification and test generation tool KeY.

Model-based testing of software and systems: recent advances and challenges

“…The third paper [10] by Christoph D. Gladisch, "Model Generation for Quantified Formulas with Application to Test Data Generation", proposes a model generation algorithm, which solves first-order logic formulae with quantifiers. The model can be converted into a test preamble for state initialization.…”