Modernizing Legacy Java System Documentation Using AI Agents
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About the client
This case study delves into the exploration journey of a our Client who wanted to refactor the legacy t code of his system through AI agenet which was based on Java 8 (limited version with Websphere 9) and having mixed frameworks Struts, JSP Page, jQuery, Vue.js technologies and frameworks.
Business Challenges
For our client, we had to overcome the following challenges to complete this complex development project:
- Massive codebase (700,000 lines) causing performance issues in the analysis
- Multiple coding styles and patterns across different periods
- Undocumented dependencies and hidden coupling
- Mixed architecture patterns from different development eras
- Undocumented foreign key relationships
- Legacy data structures
- Resource-intensive analysis processes
- Maintaining consistency across generated documentation
- Ensuring technical accuracy
Our Solutions
We developed a interrelated Ai Agents to process the tasks, do the analysis, update the code, data and required documents.
Documentation Agent
Generate technical documentation
Capabilities
- Code pattern recognition
- Natural language generation
- Context understanding
- Documentation template management
Tools Integration
- JavaDoc parser/Github-copilot/ Claude
- Markdown generator
- Version control system integration
Code Analysis Agent
Source code analysis and commenting
Capabilities
- Static code analysis
- Code complexity measurement
- Design pattern recognition
- Automated comment generation
Tools Integration
- SonarQube
- Claude
- OpenAI
Architecture Agent
UML and diagram generation
Capabilities
- Code structure analysis
- Database schema analysis
- Relationship mapping
- Diagram generation
Tools Integration
- PlantUML
- Enterprise Architect API
- Database modeling tools
Quality Assessment Agent
Code quality analysis
Capabilities
- Code smell detection
- Performance bottleneck identification
- Security vulnerability scanning
- Technical debt assessment
Tools Integration
- PMD
- SpotBugs
- OWASP dependency checker
Results & Impact
Documentation Quality
- Documentation coverage (>90%)
- Technical accuracy (>95%)
- Readability scores (>8/10)
Code Quality
- Reduced complexity (20% reduction)
- Increased test coverage (>70%)
- Reduced technical debt (30% reduction)
Cost Benefits
- Reduced maintenance time (40%)
- Improved developer onboarding (60% faster)
- Reduced bug fixing time (30%)
- Enhanced system reliability (25%)
Long-term Benefits
- Improved system maintainability
- Reduced technical debt
- Enhanced system understanding
- Faster feature development
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