Knowledge Discovery System (KDS) — Continuous Architecture and Requirements Recovery for Active and Legacy Software Systems
Keywords: CASE Tools, Software Architecture Recovery, Data Flow Analysis, Requirements Recovery, Legacy Software Modernization, Model Drift, Traceability Engineering, Digital Engineering, Systems Engineering, Reverse Engineering
The challenge has been keeping the model synchronized with reality.
For decades, software engineering has struggled with a fundamental problem.
Architectures are documented.
Requirements are documented.
Designs are documented.
Data flows are documented.
Then the software changes.
The implementation evolves while the models, diagrams, documentation, and engineering artifacts remain frozen in time.
Eventually the documentation stops matching the system.
Engineers stop trusting it.
The model becomes a historical artifact rather than a representation of reality.
This failure mode is one of the primary reasons traditional CASE (Computer-Aided Software Engineering) environments never achieved their original promise.
Knowledge Discovery System (KDS) was created to address that problem.
Rather than treating architecture, requirements, documentation, traceability, and engineering artifacts as static deliverables, KDS continuously derives them from the implementation itself, enabling regeneration as software evolves.
The objective is not documentation generation.
The objective is maintaining engineering truth.
The most significant artifact currently published in this repository is a generated Data Flow Diagram (DFD) consisting of approximately 3,883 pages.
This is not a conceptual example.
It is an actual engineering artifact generated from analysis of a real software system.
The published artifact contains:
- Recovered data flows
- Hyperlinked navigation throughout the model
- Engineering traceability structures
- Associated implementation details
- Cross-referenced engineering artifacts
Rather than describing a capability, this repository publishes generated engineering output for direct inspection.
Readers are encouraged to inspect the artifact and evaluate the results for themselves.
The challenge has never been creating documentation once.
The challenge has been keeping engineering knowledge synchronized with reality.
As software evolves:
- Documentation drifts
- Architecture drifts
- Requirements drift
- Traceability deteriorates
Eventually teams stop trusting the documentation because it no longer represents the software.
KDS is built around a different principle:
Engineering knowledge should be regenerated from implementation rather than manually maintained after the fact.
Knowledge Discovery System reconstructs engineering knowledge directly from operational software systems.
Generated artifacts include:
- Software Requirements Specifications (SRS)
- Software Design Descriptions (SDD)
- Data Flow Diagrams (DFD)
- Architectural Models
- Interface Definitions
- Memory Maps
- Traceability Matrices
- Verification and Validation Artifacts
- Modernization Documentation
The resulting artifacts provide a current, traceable, and inspectable representation of the system being analyzed.
Organizations responsible for long-lived software systems commonly encounter:
- Missing documentation
- Outdated documentation
- Incomplete requirements records
- Architecture diagrams that no longer match implementation
- Loss of institutional knowledge
- Increasing modernization risk
This challenge spans multiple sectors including:
- Aerospace and Avionics
- Defense Systems
- Medical Devices
- Industrial Control Systems
- Critical Infrastructure
- Long-Lifecycle Embedded Systems
In many cases, organizations are expected to maintain, certify, secure, modernize, or extend software they do not fully understand.
KDS approaches that challenge differently.
Rather than relying on assumptions, recollection, tribal knowledge, or probabilistic inference, KDS derives engineering knowledge directly from the implementation itself.
The goal is to establish a traceable chain of evidence connecting:
Implementation → Architecture → Requirements → Documentation → Verification Artifacts
This creates engineering artifacts that can be evaluated, inspected, traced, and regenerated from the system they describe.
Traditional reverse engineering focuses on understanding how a system works.
KDS focuses on restoring and maintaining engineering understanding throughout the lifecycle of a system.
The distinction is important.
A recovered diagram is useful.
A regeneratable engineering model is transformational.
A static representation eventually becomes obsolete.
A continuously regeneratable representation can evolve alongside the software it describes.
Historically, software models and documentation suffer from drift.
Over time, engineering artifacts become increasingly disconnected from the systems they were intended to describe.
KDS replaces static documentation with a regeneration-centric approach.
When implementation changes, engineering knowledge can be regenerated from the implementation itself.
The objective is to maintain alignment between:
- Software Implementation
- Architecture
- Requirements
- Documentation
- Verification Evidence
Engineering artifacts remain useful only so long as they remain connected to reality.
Legacy System
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Implementation Analysis
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Control Flow Recovery
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Data Flow Recovery
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Architectural Reconstruction
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Requirements Recovery
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Documentation Generation
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Traceability Generation
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Verification Artifacts
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Regeneration as the System Evolves
- CASE Tools
- Software Architecture Recovery
- Legacy Software Modernization
- Requirements Recovery
- Data Flow Analysis
- Traceability Engineering
- Model-Based Engineering (MBSE)
- Digital Engineering
- Systems Engineering
- Reverse Engineering
- Software Documentation
- Verification and Validation
- Critical Systems Engineering
- Legacy Code Analysis
- Engineering Knowledge Management
This repository contains public information describing KDS methodology together with representative engineering artifacts.
Repository contents may include:
- Generated Data Flow Diagrams
- Architectural Models
- Traceability Structures
- Reconstruction Artifacts
- Research Material
- Technical Papers
- Conference Submissions
- Methodological Documentation
Where possible, generated artifacts are published for independent inspection.
The intent is to provide evidence of results rather than descriptions of capabilities.
The central premise behind KDS is straightforward:
Engineering artifacts should derive from the system.
They should not depend on memory.
They should not depend on tribal knowledge.
They should not depend on documentation that stopped evolving years ago.
They should be regeneratable, inspectable, and traceable to the implementation they describe.
A model that cannot remain synchronized with reality will eventually lose credibility.
Engineering truth requires continuity between implementation and understanding.
KDS is an effort to establish and maintain that continuity.
- Legacy Software Reconstruction
- Architecture Recovery
- Data Flow Analysis
- Requirements Recovery
- Traceability Generation
- CASE Environment Evolution
- Documentation Synchronization
- Digital Engineering
- Model-Based Engineering
- Critical-System Modernization
The purpose of this repository is not to ask readers to accept claims.
The purpose is to publish representative artifacts, methodologies, and outputs that can be independently examined and evaluated.
Engineering credibility is established through observable results, traceability, reproducibility, and transparency.
Evidence matters more than claims.
Readers are encouraged to inspect the published artifacts and draw their own conclusions.
If you are responsible for understanding, modernizing, certifying, securing, or sustaining a legacy software system, I would be interested in discussing your use case.
Steve Whiseant
Knowledge Discovery System
See LICENSE.md for licensing information.