AI-Driven Code Documentation and Impact Analysis
Overview
The client, a financial services company with a complex legacy system built on an evolving SQL schema, faced significant challenges in updating their application due to outdated or non-existent documentation and lack of team members with knowledge of the system. Leveraging AI Large Language Models (LLMs), the goal was to generate comprehensive documentation from the legacy code to understand the system and prepare for its modernization.
Technologies:
Legacy Documentation
The client’s application was built over many years (+15 years), and the complexity of the SQL schema combined with the lack of clear documentation made system updates and modernization extremely difficult. Understanding the intricacies of the legacy code required considerable effort, making modernization costly and time-consuming. Understanding the system would enable smoother integration with contemporary technologies, enhance scalability, and streamline future updates. The client aimed to leverage AI-driven solutions to analyze code, generate documentation, and predict the impact of code changes for efficient modernization.
Implementing AI-driven documentation and impact analysis was crucial for the client to enhance the understanding of their legacy system, reduce modernization risks, and improve business alignment. This solution provided an opportunity to increase operational efficiency and ensure the system was future-proof.
Challenges
No Documentation
The lack of up-to-date documentation made it difficult to understand the system’s core functionality.
Extraneous Attributes
Decompiling the code revealed unnecessary UI attributes, which complicated AI input and processing.
Custom AI Training
Training AI to recognize domain-specific patterns in forms and SQL structures required domain adaptation.
Binary Code Format
The source code in binary format presented a barrier for AI-driven documentation generation.
Future Business Alignment
Ensuring that system changes aligned with evolving business strategies was critical for sustainable growth.
Strategic Solutions
AI-Powered Documentation Generation
AI LLMs were used to automatically generate detailed documentation by analyzing the SQL schema and source code. The generated documentation provided insights into the structure, business logic, and impact of code changes, enabling developers to modernize the system with confidence.
Scenario 1: Impact Analysis of Changes
Using AI, the client could assess the impact of adding foreign key columns to the existing database. The LLM generated a complete set of proposed code changes and identified all areas affected, mitigating risks by predicting necessary adjustments.
Scenario 2: Business Process Descriptions
The AI model successfully translated technical inputs such as SQL and XML into business-level descriptions, helping stakeholders understand the process implemented in the system without needing technical knowledge.
Binary Code Conversion
A custom process was implemented to convert the binary source code into a readable text format, allowing the AI model to process and generate accurate documentation from the forms code.
Attribute Cleanup
Extraneous UI attributes were removed from the decompiled code, improving the efficiency of AI processing and focusing on relevant information. This significantly reduced the size of files that the AI needed to process, streamlining the documentation process.
AI Training and Adaptation
The AI LLMs were trained on domain-specific SQL and form structures, ensuring it could understand the nuances of the legacy system. The AI was custom trained to handle the unique challenges presented by the client’s system, including generating insights on how future business strategy changes would affect the code.
Solution Elements
AI Documentation Generation
Automatic generation of system documentation based on existing SQL schemas and legacy code provided crucial insights for modernization.
Impact Simulation
Simulating code changes allowed for risk-free testing of system updates and streamlined change management.
Binary Code Handling
The binary format of the source code was converted into readable text, enabling AI analysis.
Custom AI Training
The AI model was trained to recognize patterns in SQL and forms unique to the client’s application, improving accuracy.
Business Alignment
AI-driven reverse engineering ensured that code changes aligned with shifting business strategies, future-proofing the system.
Process Optimization
The AI model was able to generate efficient workflows by recommending optimizations based on impact analysis.
Summary
The modernization of the client’s legacy system through AI-driven documentation and impact analysis provided significant benefits, including faster code understanding, improved change management, and alignment with future business strategies. By leveraging AI to simulate code changes and generate documentation, the client was able to modernize their system efficiently and minimize risk.
- Generated complete system documentation from legacy code using AI.
- Reduced file size by 30% through extraneous attribute cleanup.
- Achieved 100% coverage of code changes in impact analysis simulations.
- Improved documentation accuracy by 40% through custom AI training.
- Reduced time for modernization planning by 25%.