A hands-on Software Quality Engineering portfolio focused on test strategy, Python automation, API testing, database validation, security testing, DevSecOps, and AI-assisted QA.
The goal is to demonstrate how modern QA combines disciplined testing with automation, security thinking, measurable quality, and responsible use of AI.
| Project | What it demonstrates |
|---|---|
01-api-testing-framework |
Python API automation, schema validation, negative testing |
02-ui-automation |
Selenium + pytest page-object automation |
03-test-strategy |
Risk-based test strategy, traceability, coverage and exit criteria |
04-defect-management |
Defect triage, severity/priority, RCA and quality metrics |
05-database-testing |
SQL/data validation patterns and reconciliation |
06-security-api-testing |
Defensive API security checks and OWASP-oriented validation |
07-secure-python |
Python secure coding, dependency and static-analysis checks |
08-ai-assisted-qa |
AI-assisted requirements analysis, test generation and review |
09-ai-security-testing |
Defensive prompt-injection and output-safety evaluation |
10-ci-quality-gates |
GitHub Actions, pytest, Bandit and dependency auditing |
QA: pytest, Selenium, API testing, test design, risk-based testing, regression, UAT, defect management
Python: requests, pytest, JSON Schema, SQL, reusable test utilities
Security: OWASP-oriented API checks, secure coding, SAST, dependency auditing, security test design
AI: requirements-to-tests, test data generation, test review, prompt-injection evaluation, responsible AI testing
DevOps: GitHub Actions, CI quality gates, reporting
- Start with business risk and requirements, not tools.
- Combine UI, API, integration, data, and security validation.
- Automate repeatable regression while preserving exploratory testing.
- Treat defects as engineering signals: evidence, impact, root cause, trend.
- Make quality measurable with coverage, defect leakage, execution and automation metrics.
- Use AI to accelerate QA work while keeping a human review and evidence trail.
- Test only systems and environments for which you have authorization.
git clone https://github.com/anacerdan/qa-python-security-ai.git
cd qa-python-security-ai
python -m venv .venv
# Windows: .venv\\Scripts\\activate
# macOS/Linux: source .venv/bin/activate
pip install -r requirements.txt
pytest -qThis repository is designed to show progression from manual testing and test leadership → automation → security → AI-assisted quality engineering.
All security and AI-security examples are defensive and use synthetic/local test data. They do not contain credentials, exploit payloads for unauthorized targets, or production secrets.