[BPStudy] Literate Model-Driven Approach to Software Development in the AI Era

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 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.

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.

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

A standardized modeling language for object-oriented analysis and design. It represents system structures and behaviors through diagrams such as class, sequence, and use case diagrams. Serves as the foundational language for UP and CBD.

A Literate Model is a “readable model” that integrates model structure with natural-language narrative (structured documentation). It extends the idea of literate programming into the modeling domain, unifying structure (model) and narrative (structured text) into a single intelligible artifact interpretable by both humans and AI. The concept of “Literate Modeling” has been explored previously by some researchers and developers, mostly as an approach to improve documentation or code comprehension. However, those attempts did not establish a systematic modeling methodology that integrates models, narrative, and AI assistance as a unified framework. The Literate Model is a modeling concept newly systematized and proposed by SimpleModeling for the AI era. Building upon the ideas of literate modeling, it redefines them as an intelligent modeling foundation that enables AI-collaborative knowledge circulation and model generation. It is not merely a modeling technique but a framework that embeds human reasoning and design intent as narrative within the model, enabling AI to analyze and reconstruct them to assist in design and generation.

CML is a literate modeling language for describing Cozy models. It is designed as a domain-specific language (DSL) that forms the core of analysis modeling in SimpleModeling. CML allows model elements and their relationships to be described in a narrative style close to natural language, ensuring strong compatibility with AI support and automated generation. Literate models written in CML function as intermediate representations that can be transformed into design models, program code, or technical documentation.

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.

A generation technique that supplements a language model’s internal (parametric) knowledge by retrieving relevant external information before generation. RAG systems first search knowledge sources such as databases or knowledge graphs and then use the retrieved context as input for text generation.

A W3C-standardized data model that represents information as subject–predicate–object triples.

Retrieval Knowledge Base (RKB) is a structured, retrievable subset of Non-parametric Knowledge optimized for use by RAG (Retrieval-Augmented Generation). It contains indexed SmartDox documents, glossary entries, and semantic metadata, allowing AI models to fetch explicit knowledge as contextual input. Through RAG interaction, the RKB serves as a bridge for transforming external explicit knowledge into intermediate assimilated knowledge within the AI model.

A structured instruction or contextual representation that bridges retrieved knowledge (RAG) and the AI model’s reasoning process. It transforms the structured knowledge from the BoK into a narrative or directive form that the model can interpret, act upon, and internalize.

A software construct that encapsulates well-defined responsibilities, contracts, and dependencies as a reusable and replaceable unit. In the logical model, it serves as an abstract structural unit; in the physical model, it corresponds to an implementation or deployment unit.

Observation is the recorded form of phenomena that are judged worthy of being noted and stored. Observations serve as the foundation for logging, monitoring, auditing, and troubleshooting.