QA & Verification System
A repeatable QA process that reports what was checked, records the result, and separates baseline rules from behavior-focused tests.
The problem
A test result is useful only when the reviewer can tell what was checked and what the check actually establishes. QA for code, structured outputs, and written deliverables needs readable reports with explicit criteria and results.
Approach
The QA workflow applies rule-based checks to a named artifact, records each result, and returns a structured verdict. Functional behavior is checked separately with scenario and end-to-end tests.
Contribution
- Produced structured QA reports with rule-by-rule pass/fail results.
- Used syntax and content checks for implementation and case-study artifacts.
- Kept deterministic scenario tests and end-to-end behavior checks distinct from baseline format checks.
Technical implementation
A Python-based QA process evaluates configured rules and emits JSON and Markdown reports. Checks in the examples below include minimum content, required structure, and Python syntax parsing. Separate local test suites verified qualification histories and the prospecting workflow.
Evidence & verification
Results
Reports show exactly which checks passed, and the related local tests verify behavior beyond document structure. The results make the tested scope reviewable and the QA method reusable across code and portfolio artifacts.
Limitations
A PASS applies only to the listed checks and tested cases. It is not a certification or a guarantee of general correctness, production reliability, security, or defect-free behavior.