Mohsen Ahmadi (Arizona State University), Pantea Kiaei (Worcester Polytechnic Institute), Navid Emamdoost (University of Minnesota)

Mutation analysis is an effective technique to evaluate a test suite adequacy in terms of revealing unforeseen bugs in software. Traditional source- or IR-level mutation analysis is not applicable to the software only available in binary format. This paper proposes a practical binary mutation analysis via binary rewriting, along with a rich set of mutation operators to represent more realistic bugs. We implemented our approach using two state-of-the-art binary rewriting tools and evaluated its effectiveness and scalability by applying them to SPEC CPU benchmarks. Our analysis revealed that the richer mutation operators contribute to generating more diverse mutants, which, compared to previous works leads to a higher mutation score for the test harness. We also conclude that the reassembleable disassembly rewriting yields better scalability in comparison to lifting to an intermediate representation and performing a full translation.

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Kai Jansen (Ruhr University Bochum), Liang Niu (New York University), Nian Xue (New York University), Ivan Martinovic (University of Oxford), Christina Pöpper (New York University Abu Dhabi)

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Ian Martiny (University of Colorado Boulder), Gabriel Kaptchuk (Boston University), Adam Aviv (The George Washington University), Dan Roche (U.S. Naval Avademy), Eric Wustrow (University of Colorado Boulder)

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Gennaro Avitabile, Vincenzo Botta, Vincenzo Iovino, and Ivan Visconti (University of Salerno)

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Sung Ta Dinh (Arizona State University), Haehyun Cho (Arizona State University), Kyle Martin (North Carolina State University), Adam Oest (PayPal, Inc.), Kyle Zeng (Arizona State University), Alexandros Kapravelos (North Carolina State University), Gail-Joon Ahn (Arizona State University and Samsung Research), Tiffany Bao (Arizona State University), Ruoyu Wang (Arizona State University), Adam Doupe (Arizona State University),…

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