# Sprint 51 Summary: JVM Language Expansion

**Sprint**: 51
**Completion Date**: October 24, 2025
**Status**: Completed ✅

## Overview

Sprint 51 successfully expanded PMAT's language analysis capabilities to include comprehensive support for JVM languages, specifically Java and Scala. This builds upon Sprint 50's Kotlin implementation, creating a full suite of JVM language tools that enhance PMAT's polyglot analysis capabilities.

## Key Accomplishments

### 1. Java Language Support

- Implemented Java AST visitor in `server/src/services/languages/java.rs`
- Created Java AST strategy in `server/src/services/ast/languages/java.rs`
- Added integration tests in `server/tests/integration/java_integration.rs`
- Implemented Java documentation in `docs/features/JAVA-LANGUAGE-SUPPORT.md`

### 2. Scala Language Support

- Added tree-sitter-scala dependency to Cargo.toml
- Added scala-ast feature flag to Cargo.toml
- Created Scala AST visitor in `server/src/services/languages/scala.rs`
- Created Scala AST strategy in `server/src/services/ast/languages/scala.rs`
- Added integration tests in `server/tests/integration/scala_integration.rs`
- Created documentation in `docs/features/SCALA-LANGUAGE-SUPPORT.md`

### 3. MCP Tool Integration

- Created Java MCP tools in `server/src/mcp_integration/java_tools.rs`:
  - `analyze_java`: For Java code structure and complexity analysis
  - `mutation_test_java`: For Java mutation testing

- Created Scala MCP tools in `server/src/mcp_integration/scala_tools.rs`:
  - `analyze_scala`: For Scala code analysis with functional metrics
  - `mutation_test_scala`: For Scala mutation testing

- Added JVM tools registration in `server/src/mcp_integration/server.rs`
- Updated module exports in `server/src/mcp_integration/mod.rs`

### 4. Documentation

- Created comprehensive JVM tools documentation in `docs/mcp/JVM-TOOLS.md`
- Updated `docs/mcp/TOOLS.md` to reference new JVM tools
- Created Sprint 51 planning document
- Created this summary document

### 5. Testing

- Implemented integration tests in `server/src/mcp_integration/jvm_tools_integration_tests.rs`
- Tests for both analysis and mutation testing tools
- Added test fixtures for Java and Scala code analysis

## Technical Details

### Feature Flags

All JVM language support is properly feature-flagged:

- `java-ast`: For Java language support
- `scala-ast`: For Scala language support

Both flags are included in the `all-languages` and `most-languages` feature groups.

### Language-Specific Features

#### Java Analysis

- Class, interface, and method detection
- Package determination
- Complexity metrics calculation
- AST item extraction

#### Scala Analysis

- Classes, traits, objects, and case classes detection
- Functional programming metrics (percentage of functional vs imperative code)
- Pattern matching and higher-order function identification
- Detailed complexity analysis

## Metrics

- **Files Created**: 12
- **Files Modified**: 5
- **New Test Cases**: 4
- **New MCP Tools**: 4
- **Documentation Pages**: 3

## Next Steps and Future Work

### Immediate Next Steps

1. **Test Language Server Integration** - Verify JVM language analysis tools work with PMAT language server
2. **Create Demo Projects** - Create example Java and Scala projects for demonstration purposes

### Future Work (Sprint 52 Possibilities)

1. **Advanced JVM Analysis**:
   - Add bytecode analysis for JVM languages
   - Implement cross-language JVM analysis (Java/Scala interoperability)

2. **Compiler Integration**:
   - Integrate with javac and scalac for deeper analysis
   - Add compilation warning and error detection

3. **Framework-Specific Support**:
   - Add specific support for common JVM frameworks (Spring, Play, Akka)
   - Create specialized analysis tools for these frameworks

4. **Performance Optimization**:
   - Optimize JVM language analyzers for large codebases
   - Implement incremental analysis for better performance

## Conclusion

Sprint 51 successfully delivered comprehensive JVM language support, completing our roadmap goal of supporting the major JVM languages (Kotlin, Java, Scala). The implementation follows the established patterns in the PMAT architecture, ensuring consistency and maintainability.

These additions significantly enhance PMAT's polyglot analysis capabilities and provide valuable tools for developers working with JVM languages through the Model Context Protocol.

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**Author**: PMAT Development Team
**Date**: October 24, 2025