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DirectFuzz: Automated Test Generation for RTL Designs using Directed Graybox Fuzzing

The work

AuthorsSadullah Canakci; Leila Delshadtehrani; Furkan Eris; Michael Bedford Taylor; Manuel Egele; Ajay Joshi
Editors
Typemisc
Year2021
Citekeycanakci2021directfuzz

Where it appeared

Published inProceedings - ACM IEEE Design Automation Conference
PublisherAssociation for Computing Machinery
Pages529--534

From OpenAlex, which stores abstracts as an index of words and their positions: what is shown is rebuilt from that, and reads fluently whether or not it is what the author wrote.

Abstract

A critical challenge in RTL verification is to generate effective test inputs. Recently, RFUZZ proposed to use an automated software testing technique, namely Graybox Fuzzing, to effectively generate test inputs to maximize the coverage of the whole hardware design. For a scenario where a tiny fraction of a large hardware design needs to be tested, the RFUZZ approach is extremely time consuming. In this work, we present DirectFuzz, a directed test generation mechanism. DirectFuzz uses Directed Graybox Fuzzing to generate test inputs targeted towards a module instance, which enables targeted testing. Our experimental results show that DirectFuzz covers the target sites up to 17.5 × faster (2.23 × on average) than RFUZZ on a variety of RTL designs.

How it got here

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Added2026-10-24 22:10 UTC
Not denied bya person 2026-10-29 17:12 UTC

Cite it as

@misc{canakci2021directfuzz,
  title        = {DirectFuzz: Automated Test Generation for RTL Designs using Directed Graybox Fuzzing},
  author       = {Sadullah Canakci and Leila Delshadtehrani and Furkan Eris and Michael Bedford Taylor and Manuel Egele and Ajay Joshi},
  year         = {2021},
  journal      = {Proceedings - ACM IEEE Design Automation Conference},
  publisher    = {Association for Computing Machinery},
  pages        = {529--534},
  doi          = {10.1109/dac18074.2021.9586289},
}

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