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TEST_VECTORS.md

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GlitchGremlinProgram Test Vectors

1. Chaos Request Tests

1.1 Valid Requests

  • Minimum token amount request (1 token)
  • Maximum token amount request (1M tokens)
  • Typical request with all parameters (100 tokens, 5 min duration)
  • Request with optional fields omitted (no result_ref)
  • Request with maximum concurrency (100 concurrent tasks)
  • Request with minimum latency (1ms target)

1.2 Invalid Requests

  • Insufficient token balance
  • Invalid program address
  • Malformed parameters
  • Expired request

2. Escrow Tests

2.1 Valid Escrow Operations

  • Successful token transfer to escrow
  • Partial refund on completion
  • Full refund on failure
  • Escrow expiration handling

2.2 Invalid Escrow Operations

  • Double spend attempt
  • Unauthorized escrow release
  • Expired escrow release
  • Invalid token transfer

3. Governance Tests

3.1 Valid Governance Operations

  • Proposal creation with sufficient stake
  • Voting with valid tokens
  • Proposal execution on approval
  • Vote tally accuracy
  • Staking tokens for governance power
  • Delegating voting power
  • Claiming staking rewards
  • Early unstake with penalty

3.2 Invalid Governance Operations

  • Proposal with insufficient stake
  • Double voting attempt
  • Voting after deadline
  • Unauthorized proposal execution
  • Invalid staking parameters
  • Delegation to invalid address
  • Early unstake without penalty
  • Reward claim before lockup period

4. Edge Cases

4.1 Concurrency Tests

  • Multiple simultaneous requests
  • Race conditions in voting
  • Parallel escrow operations
  • High volume stress testing

4.2 Error Recovery

  • Failed request retry
  • Partial completion handling
  • Invalid state recovery
  • Network outage scenarios

5. Security Tests

5.1 Privileged Operations

  • Program upgrade verification
  • Fee structure modification
  • Escrow release authorization
  • Governance parameter changes

5.2 Attack Vectors

  • Reentrancy attempts
  • Arithmetic overflow
  • Invalid instruction data
  • Unauthorized access attempts

5.3 ML Model Tests

  • Model training validation
  • Prediction accuracy verification
  • Feature extraction testing
  • Confidence score calibration
  • Model update verification
  • Adversarial attack resistance
  • Model performance benchmarks
  • Edge case handling