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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TASE: Reducing Latency of Symbolic Execution with Transactional Memory

Adam Humphries (University of North Carolina), Kartik Cating-Subramanian (University of Colorado), Michael K. Reiter (Duke University)

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Safer Illinois and RokWall: Privacy Preserving University Health Apps...

Vikram Sharma Mailthody, James Wei, Nicholas Chen, Mohammad Behnia, Ruihao Yao, Qihao Wang, Vedant Agarwal, Churan He, Lijian Wang, Leihao Chen, Amit Agarwal, Edward Richter, Wen-mei Hwu, and Christopher Fletcher (University of Illinois at Urbana-Champaign); Jinjun Xiong (IBM); Andrew Miller and Sanjay Patel (University of Illinois at Urbana-Champaign)

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WINNIE : Fuzzing Windows Applications with Harness Synthesis and...

Jinho Jung (Georgia Institute of Technology), Stephen Tong (Georgia Institute of Technology), Hong Hu (Pennsylvania State University), Jungwon Lim (Georgia Institute of Technology), Yonghwi Jin (Georgia Institute of Technology), Taesoo Kim (Georgia Institute of Technology)

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FLTrust: Byzantine-robust Federated Learning via Trust Bootstrapping

Xiaoyu Cao (Duke University), Minghong Fang (The Ohio State University), Jia Liu (The Ohio State University), Neil Zhenqiang Gong (Duke University)

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