Automata Learning for Symbolic Execution

Bernhard Aichernig, Roderick Bloem, Masoud Ebrahimi, Martin Tappler, Johannes Winter

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review


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.
Original languageEnglish
Title of host publication2018 Formal Methods in Computer Aided Design, FMCAD 2018, Austin, TX, USA, October 30 - November 2, 2018
EditorsNikolaj Bjørner, Arie Gurfinkel
PublisherIEEE CS
Pages130 - 138
Number of pages9
ISBN (Electronic)978-0-9835678-8-2
Publication statusPublished - 2018
Event18th Conference on Formal Methods in Computer-Aided Design - Austin, United States
Duration: 30 Oct 20182 Nov 2018
Conference number: 18


Conference18th Conference on Formal Methods in Computer-Aided Design
Abbreviated titleFMCAD 2018
Country/TerritoryUnited States


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