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CAROL scheduler uses online learning to find more software bugs

By PulseAugur Editorial · [1 sources] ·

Researchers have developed CAROL, a new online learning scheduler designed to optimize ensemble fuzzing. Unlike existing schedulers that rely on fixed rules and past performance summaries, CAROL utilizes a fuzzer's current context, including reward trends, waiting times, and code coverage. This context-aware approach allows CAROL to adapt its learning rules based on whether a fuzzer is improving or stagnating, leading to the discovery of more unique bugs. In tests across nine Magma targets and five C++ programs, CAROL significantly outperformed baseline schedulers, finding previously unknown crashing defects. AI

IMPACT This context-aware learning approach could improve the efficiency of automated software testing and vulnerability discovery.

RANK_REASON The cluster describes a new research paper detailing a novel algorithm for fuzzer scheduling. [lever_c_demoted from research: ic=1 ai=0.7]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

CAROL scheduler uses online learning to find more software bugs

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The cluster describes a new research paper detailing a novel algorithm for fuzzer scheduling. [lever_c_demoted from research: ic=1 ai=0.7]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Zirui Liu, Mengfan Xu, Juan Zhai, Shenglong Yao, Shiqing Ma ·

    CAROL: Context-Aware Online Learning for Fuzzer Scheduling

    arXiv:2609.06772v1 Announce Type: cross Abstract: Ensemble fuzzing runs multiple fuzzers on a target while a scheduler allocates CPU time among them. Existing schedulers base these decisions on compact summaries of past performance and rules fixed before a campaign. Our measureme…

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