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.

View More Papers

Zoom on the Keystrokes: Exploiting Video Calls for Keystroke...

Mohd Sabra (University of Texas at San Antonio), Anindya Maiti (University of Oklahoma), Murtuza Jadliwala (University of Texas at San Antonio)

Read More

Demo #7: Automated Tracking System For LiDAR Spoofing Attacks...

Yulong Cao, Jiaxiang Ma, Kevin Fu (University of Michigan), Sara Rampazzi (University of Florida), and Z. Morley Mao (University of Michigan) Best Demo Award Runner-up ($200 cash prize)!

Read More

Investigating Graph Embedding Neural Networks with Unsupervised Features Extraction...

Luca Massarelli (Sapienza University of Rome), Giuseppe A. Di Luna (CINI - National Laboratory of Cybersecurity), Fabio Petroni (Independent Researcher), Leonardo Querzoni (Sapienza University of Rome), Roberto Baldoni (Italian Presidency of Ministry Council)

Read More

podft: On Accelerating Dynamic Taint Analysis with Precise Path...

Zhiyou Tian (Xidian University), Cong Sun (Xidian University), Dongrui Zeng (Palo Alto Networks), Gang Tan (Pennsylvania State University)

Read More