Login API Tests
Validates authentication responses, invalid credentials, and request-field validation.
QA LAB ARCHITECTURE
Quality Engineering in Practice
QA Lab is a compact Flask-based portfolio project demonstrating layered automated testing across API, application routes, and browser UI, with CI orchestration, public reporting, AI-assisted change-impact analysis, and deployment.
Capabilities
Quality Engineering Capabilities
AI-ASSISTED QA
Change-Impact Analysis with Deterministic Boundaries
Merged PR evidence
Metadata, changed files, and patches
Source-derived QA catalog
Real pytest and Playwright coverage
Structured analysis context
AI change-impact analysis
Risk, test relevance, coverage gaps, and limitations
Structured AI report
Persisted on the dedicated ai-reports branch
Read-only Flask feed
AI-Assisted QA explorer
Human review at /ai
Deterministic code owns report provenance and change statistics. pytest and Playwright remain the source of truth for execution results; public visitors can review persisted analysis but cannot trigger OpenAI.
TESTING STRATEGY
Layered Application Coverage
Flask Route Tests
Validates routing, redirects, public navigation, rendered states, and browser-form responses.
UI Tests
Validates visible login controls, success/error feedback, and native required-field behavior.
CI & REPORTING FLOW
From Automated Tests to Public Dashboard
Backend pytest job
Playwright UI job
aggregate-results
latest.json + GitHub Pages results
Flask results endpoint
QA Lab dashboard
Hosted on Render
Pull requests validate backend, browser, and aggregation jobs; production reporting is published only from main.
WHY THIS ARCHITECTURE?
Design Decisions
Separate test execution
Backend and browser tests run independently for clearer feedback and failure isolation.
One reporting source of truth
Both JUnit outputs are aggregated into one public latest.json result feed.
Sanitized public reporting
The Flask dashboard consumes the published result contract without exposing internal CI artifacts.