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Abstract:
This talk presents a novel approach to model-guided fuzzing for distributed systems, specifically leveraging TLA+ models. The method addresses the challenges of applying traditional fuzzing to distributed systems where defining meaningful inputs and coverage is complex, and the state space explosion prevents exhaustive testing. Instead of directly generating test cases from model traces, the approach uses the TLA+ model as a source of coverage information. Inputs are defined as sequences of scheduling choices (e.g., message delivery order, process failures) for the implementation. Observed events from the implementation's execution are then mapped to and simulated on a refined TLA+ model to track visited model states. This model state coverage guides input mutation in a feedback loop to explore previously unseen model states and discover bugs in the implementation. The evaluation on implementations of the Raft protocol (etcd, Redis Raft) demonstrates that using model state coverage leads to better model exploration and finds bugs, including new and rare ones, statistically faster than methods guided by line or trace coverage. Key challenges include efficient model simulation and identifying appropriate model abstractions for effective guidance.
Model-Guided Fuzzing of Distributed Systems using TLA+
0:02 Introduction: The talk introduces model-guided fuzzing for distributed systems using TLA+ models, addressing the difficulties of applying traditional fuzzing techniques to this domain.
1:13 Distributed System Challenges: Applying traditional guided testing to distributed systems is complex due to challenges in defining inputs (e.g., message order, failures) and coverage metrics.
1:43 Motivation: Implementations of distributed protocols often have bugs leading to significant downtime, highlighting the need for robust testing against specifications.
2:25 Existing Work: Reviews prior work like Jepsen (randomized network failures), PCT (probabilistic guarantees), Coyote (frameworks, reinforcement learning), and Mocket (model-based test generation).
6:19 Limitations of Enumeration: Enumerating all possible executions from a TLA+ model for testing is impractical due to state explosion and the significant effort required to instrument implementations to precisely follow model traces.
7:41 Proposed Approach: The core idea is to use the TLA+ model state coverage as feedback to guide input generation for the implementation in a fuzzing loop.
8:41 Input Definition: Inputs are defined as sequences of scheduling choices (e.g., deliver N messages to a process, crash a process, start a process), which are easier to randomly generate and mutate than specific message orderings across the entire system.
10:26 Execution Events: When an input (scheduling choices) is run on the implementation, events (e.g., specific messages delivered, node became leader) are collected, forming an event trace.
11:16 Model Simulation: The collected event trace is simulated on a TLA+ model to track the visited model states, which serve as the coverage metric.
13:04 Refined Model: To enable fast simulation, the TLA+ model is refined by fixing parameters (like number of terms/indexes) and explicitly defining actions for specific message types, allowing quicker mapping from implementation events to model transitions.
15:11 Mutation Strategy: Model state coverage guides input mutations, primarily by swapping scheduling choices within the input sequence to explore new execution paths in the implementation, aiming to reach new model states.
15:55 Abstraction Sensitivity: Initial runs showed limited improvement over random fuzzing. The key to effectiveness, especially for Raft, was introducing an abstraction in the model simulation that collapses states differing only in uninteresting details (like high term numbers), focusing coverage on unique protocol states.
17:01 Evaluation: The approach was evaluated on implementations of Raft: a micro benchmark in Coyote, etcd (Golang), and Redis Raft (C).
17:27 Coverage Results: Model-guided fuzzing achieved better model state coverage than guidance using line or trace coverage. It also maintained good line/branch coverage, suggesting it's a "win-win."
18:43 Bug Finding: Found one new bug and two known bugs in etcd, and twelve new bugs in Redis Raft. Bugs were found statistically faster, particularly rare ones, compared to other guidance methods.
20:20 Limitations: The approach requires significant effort for mapping implementation events to model actions and instrumentation. Its effectiveness is sensitive to finding the correct level of model abstraction.
21:11 Future Work: Potential future work includes systematically evaluating mutation strategies (beyond ad hoc swapping), using learning-based methods for input mutation, and automating the mapping process between implementation events and model actions.
22:21 Conformance Checking: The framework inherently supports conformance checking, verifying if an implementation's execution trace aligns with valid sequences in the TLA+ model.