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What Are Code Simulations?

Code Simulations allow you to describe test scenarios in natural language and have AI simulate what would happen before you run actual tests. This “testing before testing” approach helps you:
  • Identify potential issues early
  • Assess risk before execution
  • Validate testing strategies
  • Uncover edge cases through AI-driven analysis

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Scenario Creation

Build structured scenarios with the following components:
  • Create named scenarios for specific testing situations.
  • Provide a clear description of what you’re trying to validate or test.
  • Define starting conditions for the test, including:
    • Required setup or seed data
    • User permissions and access levels
    • System configurations needed before testing begins
  • Outline step-by-step actions for the scenario, such as:
    • User interactions or system events
    • Optional hints for each step to guide execution
    • Indicators for what to watch for or measure
  • Define what success looks like:
    • Specific behaviors or UI states
    • Data changes or persistence results
    • Final system state validation
  • Automatically generate scenarios from:
    • Existing tickets
    • Historical issues and known regressions
    • Relevant user reports tied to your codebase
Scenario Title: [Name]

Initial State:
[Describe starting conditions, data, or setup required]

Interventions:
Step 1: [User action] (Hint: [Optional guidance])
Step 2: [System interaction] (Hint: [What to watch for])
Step 3: [Expected behavior]

Expected Outcome:
[Describe the final result, state, or behavior expected]

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Simulation and Analysis

Run scenarios through AI-powered simulation — no actual code execution required.
  • The AI processes each step in your scenario.
  • Predictions are based on:
    • Your codebase
    • Historical failure patterns
    • Known dependencies and system behavior
  • Monitor simulations in real time.
  • Scenarios progress through states:
    • Pending → Running → Finished or Canceled
  • Includes progress indicators and status logs.
  • View intermediate states as the AI “walks through” each intervention.
  • Understand the assumptions, data transitions, and expected results at every stage.
  • The AI assigns a likelihood of success:
    • Unlikely
    • Uncertain
    • Very Likely
  • Ratings are based on historical outcomes and risk signals in your code.
  • Compare the AI’s predicted results with your expected outcomes.
  • Get explanations for any gaps to refine your assumptions.
  • Surface potential edge cases or untested paths before you commit to actual testing.

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Use Cases

  • Simulate user interactions and complex flows.
  • Validate responsive design, navigation, and form submissions without running full UI tests.
  • Model API calls and service interactions.
  • Analyze data validation, error handling, and integration points.
  • Predict data persistence, transaction handling, and load behavior.
  • Evaluate integrity checks and potential performance bottlenecks before load testing.

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Why It Matters

Code Simulations transform testing by providing intelligent pre-analysis, allowing teams to:
  • Refine test scenarios before committing time to execution
  • Identify risks and weak points early
  • Build more comprehensive and resilient test coverage
  • Reduce wasted time on redundant or low-value test cases

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Get Started

👉 Setup guide
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