Firebolt: Finding Bugs in Programmable Data Plane Generators


Jiamin Cao, Tsinghua University; Yu Zhou and Chen Sun, Alibaba Group; Lin He, Zhaowei Xi, and Ying Liu, Tsinghua University


Programmable data planes (DP) enable flexible customization of packet processing logic with domain-specific languages such as P4. To relieve developers from lengthy codes and tedious hardware details, many researches propose DP program generators that take high-level intents as input and automatically convert intents into DP programs. Generators must be correct, otherwise they may produce buggy programs or DP logic that is inconsistent with intents. Nevertheless, existing verification tools are designed to verify individual DP programs, not generators. They either cannot achieve high bug coverage or cannot debug generators with high scalability.

This paper presents Firebolt, a blackbox testing tool designed to dig out faults in DP program generators, including security vulnerabilities, intent violations, and generator crash. Firebolt achieves high bug coverage by using syntax-guided intent generation to construct a comprehensive, syntactically correct, and semantically valid intent set. To avoid intent explosion, Firebolt designs an intent space pruning approach that eliminates redundant intents while preserving representative ones. For high scalability, Firebolt automatically formalizes DP programs and intents for verification. We apply Firebolt to three popular open-source DP generators. Evaluation results demonstrate that Firebolt can detect 2x bugs with 0.1% to 0.01% human efforts compared to existing tools.

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@inproceedings {280726,
author = {Jiamin Cao and Yu Zhou and Chen Sun and Lin He and Zhaowei Xi and Ying Liu},
title = {Firebolt: Finding Bugs in Programmable Data Plane Generators},
booktitle = {2022 USENIX Annual Technical Conference (USENIX ATC 22)},
year = {2022},
isbn = {978-1-939133-29-32},
address = {Carlsbad, CA},
pages = {819--834},
url = {},
publisher = {USENIX Association},
month = jul

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