Enhancing Fuzzing for Better Bug Detection and Triaging
Aftab Hussain, Mohammad Amin Alipour
Software Engineering Research Group
Department of Computer Science
University of Houston
calendar_clock 2020 to 2021
construction Skills used: Python, C, delta-debugging, shell scripting, SLURM
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Software Engineering Research Group
Department of Computer Science
University of Houston
calendar_clock 2020 to 2021
construction Skills used: Python, C, delta-debugging, shell scripting, SLURM
arrow_backReturn to Projects
Designing and building techniques for improving the effectiveness and efficiency of fuzzing (automated random testing at scale) of parser and compiler libraries, image processors, and other important software tools that are used in billions of devices world-wide. By leveraging modern fuzzers like Google’s AFL, we aim to improve vulnerability detection and triaging. We carried out the following works under this project:
DIAR: Removing Uninteresting Bytes in Software Fuzzing
5th International Workshop on the Next Level of Test Automation (NEXTA 2022) at the IEEE International Conference on Software Testing Verification and Validation (ICST 2022)
FMViz: Visualizing Tests Generated by AFL at the Byte-level
arXiv 2021
Image:Freepik