Katharina Ruep, Daniel Große,
"SPINALFUZZ: Coverage-Guided Fuzzing for SpinalHDL Designs"
: European Test Symposium 2022, 2022
Original Titel:
SPINALFUZZ: Coverage-Guided Fuzzing for SpinalHDL Designs
Sprache des Titels:
Englisch
Original Buchtitel:
European Test Symposium 2022
Original Kurzfassung:
Boosting hardware design productivity is a ma-
jor plus of SpinalHDL, a Scala-based Hardware Description
Language (HDL). SpinalHDL achieves this by providing ob-
ject oriented programming, functional programming, and meta-
hardware description finally enabling the generation of Verilog
code. Despite all the advantages of SpinalHDL, verification is the
biggest challenge here as well.
In this paper, we bring Coverage-Guided Fuzzing (CGF), a well-
established software testing technique, to the SpinalHDL design
flow. We have implemented our approach SPINALFUZZ on top
of the fuzzer AFL++. We leverage Scala-features to automate as
many tasks as possible and ease the integration of fuzzing in
SpinalHDL. In the experiments we demonstrate the effectiveness
of SPINALFUZZ in comparison to Constrained Random Verifica-
tion (CRV). For a wide range of SpinalHDL designs we show that
SPINALFUZZ outperforms CRV and reaches coverage-closure.