Articles on spec-driven development, requirements engineering, and AI-ready architectures
In a spec-driven world, every change to the running system has a reason. Either the system does something it should not do, or we want it to do something new. AIUP captures this with two work item types that sit next to the main Use Case flow: Bug and Enhancement.
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One of the recurring questions I hear about Spec-Driven Development is this: where does the spec end and the implementation begin? The line is often blurry. Specs leak into class names. Implementation choices sneak into use case descriptions. After a few weeks, nobody can tell anymore what is intent and what is accident.
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With Claude Code and similar tools, I do not write much code by hand anymore. The AI does it for me. And most of the time, the result is good. For a moment this looks like the end of an era. If the AI writes the code, what is left for us? A lot. Just
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Specifications in spec-driven development must be readable by all stakeholders, not just technical teams. When business participants can understand the spec, errors surface earlier, assumptions decrease, and trust grows across the project.
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User stories alone often lack context. Use-Case 2.0, created by Ivar Jacobson and others, groups related stories under unified goals and delivers the structural clarity that AI-assisted development needs.
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Tools like Amazon Kiro, GitHub Spec Kit, and BMad Method promise structure for AI-assisted coding but struggle with brownfield enterprise systems. The AI Unified Process takes a fundamentally different approach better suited to existing codebases.
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Current AI tools focus too narrowly on code generation for developers. The real enterprise software challenge lies upstream in clarifying requirements, defining specifications, and achieving stakeholder alignment.
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Compares two approaches to AI-assisted software development. While BMAD focuses on orchestrating multiple AI agents, clear specifications form a more effective foundation than complex agent workflows.
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AI has made coding faster and cheaper, but the real challenge has shifted to understanding what systems should do. Clear requirements are now the critical bottleneck.
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Explores building integrity into systems through conceptual and perceived integrity, requiring excellent information flow, the Chief Engineer model, and robust technical practices.
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Examines whether use cases and user stories truly contain identical information by comparing them through real, non-trivial examples.
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Self-contained systems align perfectly with AI-driven development by keeping context manageable and boundaries clear.
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Comparing deterministic and iterative approaches to spec-driven development, and why iterative wins for real-world projects.
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The term "use case" is widely used but not always consistently. This article distinguishes business use cases focused on organizational behavior from system use cases defining system behavior, and shows how both contribute to AI-assisted requirements engineering.
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Spec-driven development doesn't mean waterfall. Learn how it fits naturally into agile, iterative workflows.
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AI is shifting software development focus from code to specifications. Introduces ReDevTest, where clear requirements and acceptance tests take precedence over implementation.
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System use cases provide the structured, unambiguous specifications that AI needs to generate reliable code.
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AI makes developers faster at coding, but the real bottleneck is elsewhere. AI Unified Process makes the full impact visible.
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How the IREB AI4RE micro-credential aligns with AI Unified Process principles for AI-assisted requirements engineering.
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Why jumping straight into code is the wrong approach and how system use cases create a better foundation for development.
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Designing application architectures that are optimized for AI-driven code generation and maintenance.
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True spec-driven development prioritizes stable specifications describing intent and system behavior over fragile task lists, enabling long-term sustainability over short-term productivity.
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User stories are popular in agile teams. They are short, readable, and focused on user value. For many teams, they help with planning and coordination. However, when the goal is Spec-driven Development, user stories show serious limitations. They push teams to create plans and task lists before the real requirements are clear. This makes them a
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The foundational article introducing spec-driven development with AI and its roots in proven software engineering methodologies.
Read ArticleArticles on Vaadin, jOOQ, and why they are ideal for AI-driven development
Traceability connects code to business requirements. This article explains why tracing use cases to tests matters, demonstrates the annotation-based approach used in the AI Unified Process, and introduces the AIUP Navigator IntelliJ plugin for navigating between specifications and test code.
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Showcases a feedback application built to demonstrate the AI Unified Process methodology, emphasizing that effective AI-assisted development prioritizes specifications and clear system behavior before code generation.
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How Vaadin's server-side Java UI and jOOQ's type-safe SQL combine to create an ideal stack for AI code generation.
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Vaadin's pure Java approach eliminates frontend complexity, making it the ideal framework for AI-assisted development.
Read ArticleAI Unified Process combines the best of proven methodologies with modern AI tooling
