FuzzSlice: Pruning False Positives in Static Analysis Warnings through Function-Level Fuzzing
Aniruddhan Murali, Noble Saji Mathews, Mahmoud Alfadel, Meiyappan Nagappan, Meng Xu
Abstract
Manual confirmation of static analysis reports is a daunting task. This is due to both the large number of warnings and the high density of false positives among them. Fuzzing techniques have been proposed to verify static analysis warnings. However, a major limitation is that fuzzing the whole project to reach all static analysis warnings is not feasible. This can take several days and exponential machine time to increase code coverage linearly.
Topics & Concepts
Fuzz testingFalse positive paradoxStatic analysisComputer scienceFunction (biology)PruningCrashCode (set theory)False positives and false negativesData miningMachine learningSoftwareProgramming languageSet (abstract data type)BiologyAgronomyEvolutionary biologySoftware Engineering ResearchSoftware Testing and Debugging TechniquesAdvanced Malware Detection Techniques