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This article explains how modeling, especially Model‑Driven Architecture (MDA), helps software architects identify and manage complexity by abstracting business, logical, and technical concerns into CIM, PIM, and PSM layers, and provides practical principles for prioritizing architectural decisions.
This article explains the fundamentals of software architecture, covering its definition, key components such as systems, modules, components, and subsystems, and explores vertical (layered) and horizontal (modular) architectures, design principles, complexity evaluation, cross‑platform considerations, and practical guidelines for building maintainable applications.
The article explains architectural thinking as a systematic approach to decompose problems, highlighting five core principles—abstraction, global perspective, layered design, evolutionary mindset, and standardization—that help engineers manage complexity, improve design quality, and adapt software systems over time.
After fifteen years of software development, the author learns that writing good code demands rapid trial‑and‑error, modular design, SOLID principles, early unit testing, and controlled complexity, while avoiding perfectionism, embracing stakeholder communication, and focusing on high‑ROI learning to create reusable, maintainable tools.
This article explores the fundamentals of software architecture, discussing entropy, the dual value of behavior and structure, core design principles such as SOLID, DRY, layering, orthogonality, component coupling, boundary definition, communication patterns, and modeling tools, while also offering practical guidance for building maintainable systems.
This article details Qunar's end‑to‑end digital quality measurement system, describing how over 100 indicators were defined, filtered, and organized into a hierarchical model, how the platform ingests and visualizes data, and how continuous governance and PDCA cycles improve system stability and reduce complexity.
Domain-Driven Design (DDD) is a methodology for tackling software complexity by aligning business, system, and organizational structures through strategic and tactical modeling, using techniques such as metaphors, layering, refinement, abstraction, bounded contexts, and a ubiquitous language, illustrated with real-world Tencent video system case studies.
This article explains Domain-Driven Design as a methodological approach for handling software complexity by aligning business, system, and organizational structures through modeling, detailing its core concepts, goals, key methods, and real‑world case studies from large‑scale systems.
Domain‑Driven Design is a methodological approach that refactors large, complex systems by aligning business language with architecture through metaphors, layering, refinement and abstraction, organizing code into high‑cohesion, low‑coupling bounded contexts, and applying strategic and tactical modeling—illustrated by Tencent Video case studies and contrasting lightweight versus rigorous practice styles.
This article introduces the fundamentals of enterprise architecture, defining its scope, reference models, and maturity stages, and explains how architects manage complexity, control costs, ensure SLA compliance, and apply iterative, partitioning, and simplification techniques to modernize enterprise IT systems.
The article explains the definition of complexity, analyzes its causes and how they manifest in software systems through scale and structure, and proposes domain‑driven design techniques such as bounded contexts, layered architecture, and change‑boxing to manage and reduce software complexity.
The article argues that complexity is an unavoidable aspect of software development that cannot be eliminated, only managed or transferred, and discusses how design choices, build‑tool trade‑offs, and knowledge models influence the way engineers cope with inevitable complexity.
This article explains how managing complexity by layering responsibilities and introducing an MVP architecture separates UI rendering from business logic in Android search features, illustrating the problems of high coupling and low cohesion with concrete Kotlin code examples and discussing AB testing and decorator patterns.