Meta Development System Framework

A meta-level framework that defines and designs development activities themselves. It integrates methods, processes, and architectures to build a cyclic, AI-assisted development environment.

A development process is the entire set of activities involved in building, deploying, and maintaining software systems. It encompasses planning, modeling, implementation, testing, and delivery as a systematic flow of development activities.

A standard structure of a development system materialized from the Meta Development System Framework. It connects the development method with Knowledge Modeling, Process Style, and Execution Architecture.

A development method is a conceptual framework composed of models and model transformations that define the structure and semantics of software systems. Examples include object-oriented design, domain-driven design (DDD), and model-driven development (MDD). A development method provides the structural foundation that defines what is to be built and how it should be represented.

An extension of business modeling for knowledge-driven development. It formalizes business structures and reasoning as knowledge for AI-assisted development.

A process style defines the temporal application pattern of a development method. Examples include iterative, incremental, waterfall, agile, and hybrid styles. It characterizes the order, rhythm, and cycles of development activities and deliverables.

A technical and runtime foundation supporting the development system, separating model and execution layers.

A process connecting Knowledge Modeling with software development, integrating knowledge elevation and knowledge circulation via AI.

A structural foundation defining how development is supported through technology, platform, and components.

A customized implementation of the development system for each project, derived from the reference profile.

A reference profile that templates typical development system configurations. It provides reusable baselines for customizing project-specific systems.

A concrete action or task performed within an Activity Space to move an Alpha to a more advanced state, typically producing or refining Work Products.

A key progress dimension in Essence representing an essential element of software engineering (e.g., Opportunity, Stakeholders, Requirements, Software System, Team, Work, Way of Working). Each Alpha evolves through well-defined states.

An OMG standard meta-model that captures the essentials of software engineering practices. It defines the core concepts (Alphas, Activity Spaces, Work Products) used to describe and improve development methods and processes.

A tangible result or artifact produced by activities to provide evidence of an Alpha’s state. Examples include documents, models, code, or other verifiable outputs.

A conceptual area of work in Essence where related activities are performed to advance one or more Alphas. Each Activity Space groups Activities with a shared objective.

An architecture that organizes the internal processes executed by generative AI: knowledge activation, assimilation, expression, promotion, and circulation.

A long-term integration process in which structured knowledge from the BoK is permanently incorporated into the model’s Pretrained Parametric Knowledge (PPK) through retraining or fine-tuning.

Knowledge Circulation denotes the cyclical process in which knowledge is activated, assimilated, expressed, and promoted between AI and humans, then re-activated. Through this loop, knowledge generated by AI is organized and reintegrated into the SmartDox site or BoK (Body of Knowledge), allowing the whole system to evolve and deepen AI’s understanding and generative ability.

At SimpleModeling, the core knowledge system for contextual sharing is called the BoK (Body of Knowledge). The goal of building a BoK is to enable knowledge sharing, education, AI support, automation, and decision-making assistance.

A Boundary Model defines the logical connection structure through which information and deliverables are exchanged across different processes or layers—such as knowledge modeling, process style, and execution architecture. It bridges the internal and external aspects of a development method, clarifying inputs, outputs, and dependencies.

A development paradigm that centers on knowledge, integrating AI assistance and modeling for continuous evolution.

Literate Model–Driven Development (LMDD) is a software development methodology that integrates natural-language narrative and formal model structure within a unified text-based framework. It extends conventional Model–Driven Development (MDD) by treating documentation and models as a single, consistent source of truth. In LMDD, the descriptive and structural elements of development artifacts are expressed together using the SmartDox language. From this unified representation, ModelDox extracts structural data, CML (Cozy Modeling Language) defines domain-specific models, and Cozy generates executable code, documentation, and configuration artifacts. Artificial intelligence participates in the LMDD process by analyzing the narrative context, validating structural consistency, and supporting the refinement of models and generated artifacts. All artifacts are represented in text form, ensuring traceability, version control, and interoperability within standard development environments. By defining a formally connected and machine-interpretable relationship between documentation, design, and implementation, LMDD provides a foundation for AI-assisted model–driven engineering where human authorship and automated reasoning operate on the same representational layer.

This is the reference profile of SimpleModeling. To concretely illustrate Literate Model-Driven Development with SimpleModeling, a reference profile is defined.