Reinforcement learning guided fuzz testing for a browser's HTML rendering engine

• URL: http://arxiv.org/abs/2307.14556v1
• Date: Thu, 27 Jul 2023 00:31:02 GMT
• Title: Reinforcement learning guided fuzz testing for a browser's HTML rendering engine
• Authors: Martin Sablotny, Bj{\o}rn Sand Jensen, Jeremy Singer
• Abstract summary: We propose a novel approach to combine a trained test case generator deep learning model with a double deep Q-network. The DDQN guides test case creation based on a code coverage signal. Our approach improves the code coverage performance of the underlying generator model by up to 18.5% for the Firefox HTML rendering engine.
• Score: 0.9176056742068814
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Generation-based fuzz testing can uncover various bugs and security vulnerabilities. However, compared to mutation-based fuzz testing, it takes much longer to develop a well-balanced generator that produces good test cases and decides where to break the underlying structure to exercise new code paths. We propose a novel approach to combine a trained test case generator deep learning model with a double deep Q-network (DDQN) for the first time. The DDQN guides test case creation based on a code coverage signal. Our approach improves the code coverage performance of the underlying generator model by up to 18.5\% for the Firefox HTML rendering engine compared to the baseline grammar based fuzzer.

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