Abstract
We introduce a runtime verification framework for programmable switches that complements static analysis. To evaluate our approach, we design and develop, a runtime verification system that automatically detects, localizes, and patches software bugs in P4 programs. Bugs are reported via a violation of pre-specified expected behavior that is captured by . is based on machine learning-guided fuzzing that tests P4 switch non-intrusively, i.e., without modifying the P4 program for detecting runtime bugs. This enables an automated and real-time localization and patching of bugs. We used a prototype to detect and patch existing bugs in various publicly available P4 application programs deployed on two different switch platforms, namely, behavioral model (bmv2) and Tofino. Our evaluation shows that significantly outperforms bug detection baselines while generating fewer packets and patches bugs in large P4 programs, e.g., switch.p4 without triggering any regressions.
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