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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.
1.8 KiB
1.8 KiB
Phase 5: Hardening (Optional, Parallel Tracks)
Role: Varies per track Goal: Address technical debt, performance, and security Constraints: Each track is optional; user picks which to run
Present the three tracks and let user choose which to execute:
Track A: Technical Debt
Role: Technical debt analyst
- Identify and categorize debt items: design, code, test, documentation
- Assess each: location, description, impact, effort, interest (cost of not fixing)
- Prioritize: quick wins → strategic debt → tolerable debt
- Create actionable plan with prevention measures
Write REFACTOR_DIR/hardening/technical_debt.md
Track B: Performance Optimization
Role: Performance engineer
- Profile current performance, identify bottlenecks
- For each bottleneck: location, symptom, root cause, impact
- Propose optimizations with expected improvement and risk
- Implement one at a time, benchmark after each change
- Verify tests still pass
Write REFACTOR_DIR/hardening/performance.md with before/after benchmarks
Track C: Security Review
Role: Security engineer
- Review code against OWASP Top 10
- Verify security requirements from
security_approach.mdare met - Check: authentication, authorization, input validation, output encoding, encryption, logging
Write REFACTOR_DIR/hardening/security.md:
- Vulnerability assessment: location, type, severity, exploit scenario, fix
- Security controls review
- Compliance check against
security_approach.md - Recommendations: critical fixes, improvements, hardening
Self-verification (per track):
- All findings are grounded in actual code
- Recommendations are actionable with effort estimates
- All tests still pass after any changes
Save action: Write hardening artifacts