Research published in the European Journal of AI, Computing & Informatics establishes generative AI methodologies for automated defect attribution in distributed systems, while parallel professional innovations introduce intelligent governance frameworks for cloud integration quality management.
-- As distributed system architectures expand in complexity, quality assurance faces unprecedented challenges in achieving comprehensive test coverage while maintaining real-time diagnostic precision. Traditional testing approaches relying on manually crafted scenarios fail to expose diverse fault modes in heterogeneous environments, while conventional diagnostic methods struggle with fragmented observability data across distributed nodes. This research addresses these limitations through generative AI methodologies that transform quality verification from reactive detection to intelligent, automated analysis.
The study establishes formal modeling frameworks for test generations through mathematical representation of state transitions, interaction tensors, and reachability functions. Generative AI models leverage these formal structures to expand test environments into diversified execution paths, enhancing system capability to handle dynamic runtime conditions. Multi-Source observability data, including logs, traces, metrics, and dependency metadata, integrates into unified representations, enabling causal reasoning across system modules. This approach significantly improves test scenarios coverage and root cause location accuracy compared to traditional pattern matching methods, while reducing diagnostic search space through structured dependency analysis.
Implementation validation demonstrates the methodology’s effectiveness in complex operational scenarios, including stable load conditions, burst traffic patterns, and cascading failure situations. Framework testing revealed enhanced fault detection capabilities across distributed architectures, with causal inference mechanisms successfully identifying root cause nodes even under conditions of incomplete data or high noise interference. The approach maintains diagnostic stability through propagation delay consistency measures, filtering pseudo-correlations that only exhibit statistical association without genuine causality.
Contributing to this research is Mingde Guo, holding a Master of Business Administration from China Europe International Business School and a Bachelor of Computer Science from China University of Geoscience. Technical expertise spans cloud native test frameworks, generative AI integration, and distributed system validation. Professional certifications encompass end-to-end automation infrastructure, GenAI-powered QA tooling, CI/CD-driven quality gates, and scalable test environment design
Mingde Guo’s current technical leadership at Amazon includes architecting unified automation infrastructure integrating embedded and backend validation, developing GenAI-powered bug triage, achieving 90% automatic routing, implementing LLM-based frameworks, measuring 1,000+ daily metrics, detecting 100+ bugs, and designing frameworks handling non-deterministic operations. Previous contributions at Stars Group established an automation strategy across web, mobile, and desktop platforms. Leadership at SecureFact built high-performing QA teams, transitioning processes from manual to intensive automation.
The integration of generative AI research with production-scale quality engineering demonstrates how formal methodologies translate into industrial impact. By establishing systematic test generation approaches while deploying adaptive verification frameworks supporting global platforms, this work bridges theoretical innovation with practical quality assurance, addressing scalability and reliability challenges facing modern distributed systems through intelligent automation, delivering improvements in mission critical cloud native environments.
Contact Info:
Name: Mingde Guo
Email: Send Email
Organization: Mingde Guo
Website: https://scholar.google.com/citations?hl=en&view_op=list_works&gmla=AElLoL2Tyxm8kZ_YcSY6lHJok6Xj4CtxtjSd2yS075AfbMfGcpck35nMlLWAGQ5g_4wJacDNb-5IlwkwEAW7gois0ZTDCa55N1pNmjvtxIA&user=RiwVRlwAAAAJ
Release ID: 89181966
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