As software systems continue to grow in scale and complexity, traditional approaches to Quality Engineering struggle to keep pace with increasingly rapid development cycles, distributed architectures, and evolving business requirements. Test automation has significantly improved software validation, but many quality engineering activities still depend on manual effort, fragmented tools, and extensive domain knowledge.

The AI-driven QE Platform is a personal initiative to explore how Artificial Intelligence can augment the entire Quality Engineering lifecycle rather than focusing solely on automated test execution. The objective is to investigate how AI can assist engineers in understanding requirements, generating meaningful test scenarios, identifying coverage gaps, analysing failures, validating system behaviour, and accelerating root cause analysis.

Rather than replacing existing Quality Engineering practices, the platform aims to enhance them by combining modern AI capabilities with established engineering principles. By reducing repetitive effort and providing intelligent assistance throughout the software delivery lifecycle, Quality Engineers can spend more time improving product quality and less time performing manual analysis.

This project serves as a research and experimentation platform for applying Enterprise AI to Quality Engineering. The engineering concepts, implementation approaches, architectural decisions, and lessons learned are documented throughout the Journal.

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Technology Stack

• Python
• pytest
• Playwright
• LangGraph
• LiteLLM
• pgvector
• Kafka
• Docker