2022•Unpublished venueRequires access

Improving Fuzzing Coverage with Execution Path Length Selection

Wenxi Zhang, Kazunori Sakamoto, Hironori Washizaki, Yoshiaki Fukazawa

Abstract

Coverage-guided fuzzing is one of the most effective types of fuzz testing. Code coverage is an important parameter of performance evaluation of the coverage-guided fuzzing tools since normally higher coverage result means a higher chance of fault detection. To expand the overall code covered, based on previous basic block analysis, we propose a method for selecting the mutants of inputs that are able to execute some specific length of the execution path.

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Available abstract

Key concepts: Fuzz testing, Computer science, Code coverage, Path (computing), Block (permutation group theory), Selection (genetic algorithm), Code (set theory), Process (computing)