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Valkey (forked from Redis) is expected to be fast and correct under an enormous variety of workloads, yet many of the nastiest bugs live outside the reach of traditional unit and integration tests. In this talk, we’ll demonstrate the Chaos Fuzzer for Valkey, built to systematically explore edge cases in the Valkey’s cluster bus, a fundamental part of valkey’s cluster communication model. We’ll show how introducing controlled and uncontrolled chaos into Valkey uncovers correctness issues, subtle crashes, and regression risks that only emerge under common real-world scenarios.
Additionally, we’ll explore how we used Agentic AI to scale fuzzing effectiveness. We will discuss how we leveraged custom AI agents to read, summarize and validate logs from the nodes in Valkey cluster to automatically identify the reasons for cluster failures and expose new bugs in Valkey. We’ll also discuss how AI can be used to generate new fuzzing inputs and scenarios based on prior failures, allowing the fuzzer to iteratively focus on the most error-prone scenarios.
In the end, we will close the talk by discussing how contributors can use this framework to deliver quality features and fixes.
