Automated Legacy System Documentation and Impact Analysis Solution for Modernization

Financial services

Challenges with Legacy System Comprehension and Modernization Risks

The client faces difficulties updating their longstanding legacy application due to a lack of up-to-date documentation, complex and poorly documented SQL schemas, and binary source code in unreadable formats. This hampers system understanding, increases modernization costs, and elevates risk exposure, especially when aligning system changes with shifting business strategies.

About the Client

A large enterprise financial organization with complex legacy systems built on extensive SQL schemas seeking modernization to improve system understanding and ensure alignment with evolving business strategies.

Goals for Streamlining Legacy System Modernization and Futureproofing

Generate comprehensive documentation for the legacy application using AI-driven analysis of SQL schemas and source code, reducing dependency on manual documentation efforts.
Simulate code changes leveraging AI to assess impact and mitigate risks associated with system updates.
Improve documentation accuracy and completeness to facilitate faster understanding and decision-making, targeting at least 40% improvement in documentation quality.
Reduce file sizes and data processing overhead by cleaning extraneous UI attributes, aiming for a 30% reduction.
Achieve full impact coverage in change simulations to ensure no unintended effects during modernization.
Align system modifications with future business strategies, enabling sustainable growth and operational efficiency.

Core Functional Capabilities for System Analysis and Documentation

AI-powered automation for comprehensive system documentation generation from legacy code and schemas.
Impact simulation engine capable of predicting effects of code modifications, including database schema changes and UI attribute updates.
Binary code conversion module to transform unreadable binary source code into human-readable text for analysis.
Attribute cleanup process to remove extraneous UI elements, optimizing AI processing efficiency.
Custom AI training on domain-specific SQL structures and form layouts to enhance accuracy and relevance.
Business process translation feature converting technical syntax into comprehensible business process descriptions.
Impact coverage dashboard to ensure all potential change effects are assessed.

Technology Stack and Architectural Approach

AI Large Language Models (LLMs) trained on domain-specific SQL and form data

Automated code analysis and documentation generation tools

Binary to text conversion utilities

Impact simulation and impact analysis modules

Machine learning models adapted for legacy system understanding

External Systems and Data Sources Integration Needs

Legacy SQL databases for schema extraction
Binary code source repositories for code conversion
Existing development and version control systems for change impact testing

Performance, Security, and Scalability Specifications

System should process legacy code and schemas efficiently, targeting a 25% reduction in modernization planning time.
High accuracy in documentation generation with at least 40% improvement over baseline manual methods.
Impact analysis coverage of 100% of applicable code pathways.
Secure handling of sensitive legacy data with compliance to applicable data protection standards.

Expected Business Benefits and Strategic Outcomes

Implementing this AI-driven system documentation and impact analysis solution is projected to significantly streamline legacy system modernization efforts, reducing planning time by approximately 25%, ensuring comprehensive impact coverage, and enhancing documentation accuracy by over 40%. This will enable better change management, reduce risks associated with system updates, and align IT systems more closely with evolving business strategies, ultimately resulting in increased operational efficiency and future readiness.