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ai-training/_docs/02_document/tests/resource-limit-tests.md
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Oleksandr Bezdieniezhnykh a47fa135de Update configuration and test structure for improved clarity and functionality
- Modified `.gitignore` to include test fixture data while excluding test results.
- Updated `config.yaml` to change the model from 'yolo11m.yaml' to 'yolo26m.pt'.
- Enhanced `.cursor/rules/coderule.mdc` with additional guidelines for test environment consistency and infrastructure handling.
- Revised autopilot state management in `_docs/_autopilot_state.md` to reflect current progress and tasks.
- Removed outdated augmentation tests and adjusted dataset formation tests to align with the new structure.

These changes streamline the configuration and testing processes, ensuring better organization and clarity in the project.
2026-03-28 06:11:55 +02:00

940 B
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Resource Limit Test Scenarios

RL-DSF-01: Dataset split ratios sum to 100%

  • Input: Any number of images
  • Action: Check train_set + valid_set + test_set
  • Expected: Equals 100
  • Traces: AC: 70/20/10 split

RL-DSF-02: No data duplication across splits

  • Input: 100 images
  • Action: Run form_dataset(), collect all filenames across train/valid/test
  • Expected: No filename appears in more than one split
  • Traces: AC: Dataset integrity

RL-ENC-01: Encrypted output size bounded

  • Input: N bytes plaintext
  • Action: Encrypt
  • Expected: Ciphertext size ≤ N + 32 bytes (16 IV + up to 16 padding)
  • Traces: Restriction: AES-256-CBC overhead

RL-CLS-01: Total class count is exactly 80

  • Input: classes.json
  • Action: Generate class list for YAML
  • Expected: Exactly 80 entries (17 named × 3 weather + 29 placeholders = 80)
  • Traces: AC: 80 total class slots