mirror of
https://github.com/azaion/ai-training.git
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Oleksandr Bezdieniezhnykh
142c6c4de8
- Replaced module-level path variables in constants.py with a structured Pydantic Config class. - Updated all relevant modules (train.py, augmentation.py, exports.py, dataset-visualiser.py, manual_run.py) to access paths through the new config structure. - Fixed bugs related to image processing and model saving. - Enhanced test infrastructure to accommodate the new configuration approach. This refactor improves code maintainability and clarity by centralizing configuration management.
46 lines
1.5 KiB
Markdown
46 lines
1.5 KiB
Markdown
# Phase 4: Execution
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**Role**: Software architect and developer
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**Goal**: Analyze coupling and execute decoupling changes
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**Constraints**: Small incremental changes; tests must stay green after every change
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## 4a. Analyze Coupling
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1. Analyze coupling between components/modules
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2. Map dependencies (direct and transitive)
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3. Identify circular dependencies
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4. Form decoupling strategy
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Write `REFACTOR_DIR/coupling_analysis.md`:
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- Dependency graph (Mermaid)
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- Coupling metrics per component
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- Problem areas: components involved, coupling type, severity, impact
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- Decoupling strategy: priority order, proposed interfaces/abstractions, effort estimates
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**BLOCKING**: Present coupling analysis to user. Do NOT proceed until user confirms strategy.
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## 4b. Execute Decoupling
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For each change in the decoupling strategy:
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1. Implement the change
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2. Run blackbox tests
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3. Fix any failures
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4. Commit with descriptive message
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Address code smells encountered: long methods, large classes, duplicate code, dead code, magic numbers.
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Write `REFACTOR_DIR/execution_log.md`:
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- Change description, files affected, test status per change
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- Before/after metrics comparison against baseline
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**Self-verification**:
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- [ ] All tests still pass after execution
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- [ ] No circular dependencies remain (or reduced per plan)
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- [ ] Code smells addressed
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- [ ] Metrics improved compared to baseline
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**Save action**: Write execution artifacts
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**BLOCKING**: Present execution summary to user. Do NOT proceed until user confirms.
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