Stress Testing

The core of stress testing involves defining and modeling hypothetical adverse scenarios. These are not predictions but plausible worst-case events used to probe system weaknesses.

• Historical Scenarios: Replay past crises (e.g., 2008 financial crisis, 2022 crypto contagion).
• Hypothetical Scenarios: Model unprecedented events (e.g., simultaneous exchange failure, 90% ETH price drop).
• Reverse Stress Tests: Start with a catastrophic outcome (e.g., protocol insolvency) and work backward to identify the sequence of failures.

Tests apply extreme, simultaneous shocks to key input variables to measure their impact on system health and solvency.

• Market Shocks: Extreme volatility, liquidity drying up, and sharp price declines across correlated assets.
• Network Shocks: Soaring gas fees, sudden spikes in transaction volume, or validator churn.
• Protocol-Specific Shocks: Parameter changes like a dramatic shift in a lending protocol's loan-to-value (LTV) ratio or a sharp increase in a stablecoin's redemption fee.

Stress tests determine if a protocol or institution can meet its obligations under duress. This is a binary test of survival.

• Liquidity Risk: Can assets be sold or used as collateral without causing catastrophic price impact? Tests for slippage and funding gaps.
• Solvency Risk: Does the entity's total asset value remain above its total liabilities? A key metric is the capital adequacy ratio.
• In DeFi, this often involves testing liquidation engines under high congestion to see if bad debt accumulates.

Evaluates how failure or stress in one component propagates through interconnected systems. This is critical for understanding systemic risk.

• Counterparty Exposure: Tests the cascading effects of a major counterparty (e.g., a large borrower, validator pool, or oracle) failing.
• Asset Correlation Breakdown: Models scenarios where normally uncorrelated assets suddenly move together, breaking diversification assumptions.
• Example: Simulating the failure of a major stablecoin to assess its impact on lending markets, DEX pools, and derivative positions.

A complementary technique that measures how much a system's output (e.g., profit, TVL, solvency) changes in response to variations in a single input parameter, holding others constant.

• Purpose: Identifies which variables are the most critical risk drivers.
• Process: 'What-if' analysis, such as observing the impact of a 10% increase in volatility or a 5% decrease in collateral value.
• Difference from Stress Testing: Sensitivity analysis changes one variable at a time; stress testing applies multiple extreme shocks simultaneously.

A performance test that evaluates a system's behavior under expected normal and peak load conditions. It measures metrics like throughput, response time, and resource utilization to establish a performance baseline.

• Goal: Validate if the system meets performance requirements.
• Contrast: Unlike stress testing, which pushes beyond limits, load testing operates within the designed capacity.

The discipline of experimenting on a system in production to build confidence in its capability to withstand turbulent conditions. It proactively injects failures like node outages, network latency, or dependency failures.

• Principle: "If you know the system will fail, you can build a more resilient one."
• Tool Example: Chaos Mesh, LitmusChaos, and Gremlin are used to automate fault injection.

The rate at which a system successfully processes transactions or operations, typically measured in transactions per second (TPS). It is a key metric in both load and stress testing.

• In Stress Tests: Throughput is monitored as load increases to identify the saturation point where it plateaus or degrades.
• Blockchain Context: A critical metric for evaluating layer-1 and layer-2 scaling solutions.

A specific way in which a system can fail. Stress testing aims to identify and analyze these modes, such as transaction pool congestion, state bloat, gas price spikes, or consensus halts.

• Analysis: Understanding failure modes allows teams to implement targeted mitigations and circuit breakers.
• Outcome: The goal is to ensure failures are graceful and predictable, not catastrophic.

The maximum operational capacity of a system before performance degrades or becomes unstable. It is a key discovery of stress testing.

• Identification: Found when metrics like latency increase exponentially while throughput plateaus.
• Use Case: Defines the upper limit for safe operation and informs auto-scaling policies and capacity planning.

An automated software testing technique that involves providing invalid, unexpected, or random data ("fuzz") as inputs to a program. It is used to discover coding errors, security vulnerabilities, and crash conditions.

• Application: In blockchain, fuzzing is critical for testing smart contract logic, consensus engines, and P2P protocols with malformed inputs.
• Tool Example: AFL++, libFuzzer.