Development Process

The Static Model Harness provides a foundation for describing domain concepts, relationships, and rules as models and connecting them to checking and execution. Part 3 of this series explains how established software engineering practices can make business constraints effective during AI-assisted generation and modification.

2026-10-05

The Model Harness describes software close to domain knowledge and constrains generation and change through processor checks and execution. Correspondence with requirements also supports checking the software's purpose. Part 3 develops static structure through concepts, relationships, and rules; Part 4 develops dynamic behavior, including state machines.

2026-09-28

An AI Development Harness connects AI interpretation, exploration, and completion with the results of software engineering, including models, architecture, execution, and verification. It makes the judgments supplied by AI explicit and verifiable, then executes the adopted meaning through programs and runtimes. The series shows how different harnesses can be combined to bring new AI applications and development speed to business-application-scale development.

2026-09-21

Domain Models and Application Models view the same subject world through different purposes and concerns. Applications for different needs use a Domain Model that accumulates understanding of the subject world. Use Case Scenarios are made concrete through Collaborations and Interactions. We review their mappings to Services and Operations and describe the approved executable Model in CML.

2026-09-14

Part 8 starts with terms and concepts, then uses Contexts, Bounded Contexts, and Ubiquitous Language to partition the problem domain and organize a Domain Model from the Object Model, Knowledge Model, and Literate Model. In SimpleModeling, generative AI consults literate information and the Knowledge Model through textus-bok, primarily generating and updating the Object Model. Developers use the visualization, diffs, evidence, impact information, and validation results provided by textus-cbd-support to review the Model and return feedback to generative AI. This iterative development loop improves the Model's validity.

2026-09-07

Literate Modeling treats organizing Stories as Narratives as an important form while also organizing Vision, requirements, decision records, existing specifications, and conventional documents that explain Domain Model concepts, rules, and relationships in natural language as Literate Models with explicit meanings, identities, and mappings to other Model elements. SmartDox is the full-spec primary description language, while Markdown is also allowed. textus-bok turns Literate Models into Knowledge Models and connects them to generative AI, which principally generates, updates, and manages the Object Model. People review the results through the visualizations, differences, and evidence provided by textus-cbd-support, then return approval or findings to the AI. Literate Models also serve as authoritative sources for facts from which requirements specifications, user guides, design explanations, and other purpose-specific documents can be composed.

2026-08-31

A Knowledge Model is a constituent of a SimpleModeling Model that supports defining Models through CML, reviewing a Domain Model, creating and reviewing a Use Case Model, and answering inquiries for Domain understanding. Terms defined in the Glossary map occurrences of the same concept across Object, Knowledge, and Literate Models. Generative AI presents candidates and explanations; people approve their meaning and evidence.

2026-08-24

A SimpleModeling Model is composed of Object, Knowledge, and Literate Models, which are combined by purpose into Domain, Use Case, and Application Models. The Object Model contains an Executable Model that defines general structure and Behavior and an Execution Example Model that presents concrete executions such as Interactions. Execution examples provide inductive constraints for design and validation, while current CML targets only the Executable Model. CML, Cozy, and Textus absorb platform-oriented mechanisms so Object Modeling can focus on describing the Domain Model.

2026-08-17

Part 4 takes the Model as its subject and composes Capabilities and Aspects through the Domain Model and Use Case Model axes. The Object Model makes the formal structure concrete, while purpose- and concern-selected Views support understanding, evaluating, and applying the complex Model.

2026-08-10

A Knowledge System collects, organizes, and classifies existing technologies and provides a map for understanding them. A Software Development Methodology selects the needed technologies from that knowledge, defines their roles and use, and guides practice. A SimpleModeling Model is composed of an Object Model for formal structure and execution examples, a Knowledge Model for generative-AI knowledge structures, and a Literate Model for human-readable context and intent. Domain Modeling organizes the three constituents with emphasis on problem-domain meaning, structure, rules, and boundaries. Application Modeling organizes them with emphasis on Use Case realization, Collaborations, Interactions, state transitions, Events, Services, and Operations. Domain Modeling is not entirely static, and Application Modeling does not own every dynamic element. Approved executable formal structures are connected to executable software through CML, Cozy, AI, and Textus as Model Realization. Quality attributes remain cross-cutting design concerns.

2026-08-03

SimpleModeling has built a path from models to executable software through CML, DSLs, Literate Models, Cozy, and Textus. Cozy transforms CML into executable software, while AI fills implementation gaps that Cozy cannot transform completely. Textus runs the resulting software. Meanwhile, people have performed most of the work of constructing a Domain Model from knowledge. With the BoK as a shared foundation for collaboration among domain experts, developers, and AI, the path from Knowledge to Executable Software can be treated as one methodology.

2026-07-27

With the arrival of AI, the domain model has become a working abstraction with a realization path to implementation that can directly drive development forward. AI translates domain models into implementations and fills in the necessary implementation details. The primary subject handled by humans therefore shifts from describing implementation step by step to software models representing the target world, responsibilities, execution, and knowledge. Because model quality strongly influences software quality, modeling becomes the new choke point, requiring existing software engineering to be reconstructed as a modeling-centered methodology.

2026-07-20

SimpleModeling adopts an architecture-centric approach derived from the Unified Process. Architecture is treated not merely as a design artifact, but as the organizing structure that guides requirements, analysis, design, implementation, and operation. By applying architectural viewpoints from the earliest stages, models become more coherent, analyzable, and AI-friendly.

2026-05-11