Refining Fuzzed Crashing Inputs for Better Fault Diagnosis
Kieun Kim (1), Seongmin Lee (2), Shin Hong (1) ((1) Chungbuk National, University, (2) Max Planck Institute for Security, Privacy)

TL;DR
DiffMin is a technique that refines crashing inputs from fuzzing to improve fault localization accuracy, aiding developers in debugging by minimizing differences between crashing and passing inputs.
Contribution
It introduces DiffMin, a novel method for refining fuzzed crashing inputs to better support fault diagnosis and debugging.
Findings
DiffMin effectively minimizes differences between crashing and passing inputs.
Enhances accuracy of spectrum-based fault localization.
Potential as a pre-debugging step after greybox fuzzing.
Abstract
We present DiffMin, a technique that refines a fuzzed crashing input to gain greater similarities to given passing inputs to help developers analyze the crashing input to identify the failure-inducing condition and locate buggy code for debugging. DiffMin iteratively applies edit actions to transform a fuzzed input while preserving the crash behavior. Our pilot study with the Magma benchmark demonstrates that DiffMin effectively minimizes the differences between crashing and passing inputs while enhancing the accuracy of spectrum-based fault localization, highlighting its potential as a valuable pre-debugging step after greybox fuzzing.
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Taxonomy
TopicsAnomaly Detection Techniques and Applications · Software Testing and Debugging Techniques · Software System Performance and Reliability
