Registered user since Tue 17 Jun 2025
Jose Sosa Rodriguez is a Software Engineer at Microsoft and a doctoral researcher in Software Engineering at the University of Arizona, where his work focuses on Software Architecture Reconstruction (SAR) and the interplay between organizational structures and software design. His research investigates how patterns of developer interaction, cross‑team communication, and socio‑technical dynamics shape the evolution of large, distributed enterprise systems. He is especially interested in using machine learning, AI‑powered analysis, and emerging quantum software engineering principles to reconstruct architectural views from multi‑repository codebases and to infer organizational realities from technical artifacts.
At Microsoft, Jose develops and enhances Azure-based application services with a focus on DevSecOps pipeline maturity, C# ASP.NET Core architecture, security and resiliency through TDD, and containerization using Docker and Kubernetes. He has led initiatives in virtualization, automation, and DevSecOps modernization, and he regularly mentors junior engineers. His technical work spans cloud-native engineering, distributed systems, secure development processes, and event-driven architectural styles—his favorite being Implicit Invocation. Jose’s academic path builds on a Bachelor’s degree in Computer Science and continues through his PhD studies, where he explores architectural patterns, design patterns, organizational coupling, and informal versus formal testing strategies in quantum and classical software systems. He is the founder of Q‑PAD: Quantum Patterns, Architecture, and Design, a research line examining architectural and design patterns in quantum software and the limits of informal testing at the intersection of classical and quantum computation.
He has published peer‑reviewed research, earning third place in the student paper competition at the CISOSE 2025 conference—and is actively expanding his research portfolio in software architecture, socio‑technical systems, and AI‑assisted organizational analysis. His current work includes developing a system that analyzes GitHub activity, ticketing artifacts, and cross-repository interactions to infer organizational structures, measure microservice coupling, and identify discrepancies between formal and emergent team topologies using automated architecture inference.
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