AI Visual Compare
AI-assisted UI comparison tool that detects visual differences between screenshots using OpenCV and LLM reasoning.
- Python
- OpenCV
- Gemini
- AI
Mehmet Serhat Ozdursun
13+ Years in Quality Engineering
QA Help
Curated hands-on resources for QA engineers, including automation practice and ISTQB sample exam pages.
Certificates & Achievements
Skills
Mehmet Serhat Ozdursun
QA Manager & Lead QA Automation Engineer | AI-Driven Quality Engineering | Mobile, Web & API | CI/CD
AT*SQA Advisory Board 2026 | ISTQB® CTAL-TM, CTAL-TAE, CTFL | Scrum.org PSD I
13+ Years in Quality Engineering
Email: serhat.ozdursun@gmail.com
Phone: +905368361407
Languages: Turkish (Native), English (C1), Spanish (B)
QA Manager & Lead QA Automation Engineer with 13+ years of experience delivering end-to-end quality engineering across mobile, web, and API platforms.
I combine people management and QA strategy with hands-on technical leadership in automation architecture, performance testing, and CI/CD quality engineering.
Throughout my career, I have led QA initiatives across fintech, SaaS, and enterprise systems, increasing release confidence, reducing production defects, and improving delivery speed.
I lead AI adoption in Quality Engineering, designing reusable agent and skill architectures for workflows spanning requirements analysis, test design, automation generation, failure analysis, and defect management, with a focus on security, maintainability, and governance.
Since July 2026, I have worked as QA Manager, AI & Testing Innovation at PulsePoint, leading a globally distributed QA engineering team and partnering with Engineering, Product, DevOps, and Architecture teams to align quality initiatives with business and technology objectives.
Value & Navigation
Professional Portfolio + QA Resource Hub
Explore professional credentials, connect directly, and use practical QA learning resources from one curated homepage.
Download ResumeView full career history and achievements.Mentorship & Knowledge SharingCareer direction, automation roadmap, and interview preparation for QA engineers.Automation PracticeInteractive UI automation playground for QA workflows.AT*SQA Advisory Board 2026View advisory board recognition and public listing.
Engineering Projects
Real Engineering Work
Hands-on projects that demonstrate practical architecture, QA discipline, and engineering execution.
AI-Assisted Mobile Test Automation with Maestro
Deterministic-first mobile test orchestration for scenario exploration, AI-assisted step generation, and on-device validation using Maestro.
- Python
- Maestro
- Mobile Testing
- AI
- Test Automation
QA Engineering CI/CD Playground
The repository behind my personal website, used to demonstrate QA engineering best practices in CI/CD including GitHub Actions, unit and component tests, SonarQube, Danger-driven AI PR review, and AI-based QA-affected area analysis.
- Next.js
- TypeScript
- GitHub Actions
- SonarQube
- DangerJS
- AI
BDD Testing Frameworks
A unified BDD-style automation framework for API, web, and mobile testing. Although it is an older project, it reflects my early approach to building scalable and reusable test automation architectures across multiple layers.
- Java
- BDD
- API Testing
- Web Testing
- Mobile Testing
Open Source Contributions
Ecosystem Contributions
Visible contributions and exploration across well-known test automation ecosystems.
WebdriverIO
Contribution and exploration related to IPC communication refactoring in the WebdriverIO test runner ecosystem.
- TypeScript
- WebdriverIO
- Test Runner Architecture
Gauge
Contributor to the Gauge test automation framework ecosystem.
- Java
- Test Automation
- Open Source
Articles & Knowledge Sharing
Thought Leadership in QA and AI
Practical writing focused on QA engineering, delivery risk, and AI-driven quality strategy.
What If AI Could Tell QA What Your Pull Request Might Break?
Explores how AI can analyze pull requests and help QA predict impacted test areas earlier in the delivery process.
- AI in QA
- Pull Request Impact Analysis
- Test Coverage Awareness
Self-Healing Locators That Report Themselves
Explores a smarter approach to UI test stability using self-healing locators combined with reporting mechanisms to reduce flaky test failures and improve reliability.
- UI Test Automation
- Self-Healing Locators
- Test Stability
The Self-Healing Pipeline: Balancing the Magic of AI Agents with Engineering Discipline
Documents an agentic QA workflow that intercepts failing CI gates, heals broken tests via MCP-backed debugging, and enforces linting and human review guardrails to prevent architectural decay.
- AI Agents
- CI/CD Quality Gates
- Test Automation
- Engineering Guardrails
The True Cost of "Autonomous" QA Agents (And What the Viral Diagrams Ignore)
Contrasts ungated self-healing loops with scoped, MCP-validated repairs, and argues that locator discipline, PR guardrails, and static analysis must come before autonomous E2E agents.
- AI in QA
- E2E Automation
- Self-Healing Tests
- Cost Awareness
What I'm Currently Exploring
- Applying AI-assisted automation workflows for PR impact analysis and intelligent test selection.
- Designing AI-assisted visual regression pipelines to detect meaningful UI risk with lower review overhead.
- Building Maestro-based mobile automation flows and validating them on real devices for production-like confidence.
- Designing an AI orchestration layer that converts natural-language test intent into executable, maintainable test flows.
- Leveraging open-source contribution work in WebdriverIO to strengthen reusable automation patterns and execution reliability.
Professional Experience