Automata Learning for Symbolic Execution

Black-box components conceal parts of software execution paths, which makes systematic testing, e. g., via symbolic execution, difficult. In this paper, we use automata learning to facilitate symbolic execution in the presence of black-box components. We substitute black-boxes in a software system with learned automata that model them, enabling us to symbolically execute program paths that run through black-boxes. We show that applying the approach on real-world software systems incorporating black-boxes increases code coverage when compared to standard techniques.