# Sprint 52 Plan: Cross-Language Analysis Enhancement

**Sprint**: 52
**Planning Date**: October 24, 2025
**Duration**: 2 weeks (Oct 27 - Nov 10, 2025)
**Status**: Planning 📋

## Overview

Following the successful implementation of all major JVM languages (Kotlin in Sprint 50, Java and Scala in Sprint 51), Sprint 52 will focus on enhancing PMAT's cross-language analysis capabilities. This sprint will develop more sophisticated polyglot analysis tools that can understand relationships between different languages within a single project.

## Goals

1. **Cross-Language Dependency Analysis**: Create tooling to detect and analyze dependencies between different languages in a polyglot project.

2. **Unified Project Analysis**: Implement a unified analysis framework that can process an entire project regardless of the mix of languages used.

3. **Polyglot MCP Tools**: Develop MCP tools specifically designed for polyglot projects.

4. **Performance Optimization**: Optimize language analyzers for better performance with large, multi-language codebases.

## Technical Approach

### 1. Cross-Language Graph Builder

Create a unified dependency graph builder that can represent relationships across language boundaries:

- Detect imports/references across language boundaries
- Track cross-language inheritance and implementation
- Map FFI/interop boundaries
- Create visualizations of cross-language dependencies

**Implementation files**:
- `server/src/graph/polyglot_graph_builder.rs`
- `server/src/graph/language_bridge_detector.rs`
- `server/src/graph/cross_language_dependencies.rs`

### 2. Unified AST Representation

Enhance the unified AST framework to better handle cross-language concepts:

- Create common representation of cross-language patterns
- Implement interfaces to map between language-specific and unified AST nodes
- Add cross-language navigability

**Implementation files**:
- `server/src/ast/polyglot/mod.rs`
- `server/src/ast/polyglot/unified_node.rs`
- `server/src/ast/polyglot/language_mapper.rs`

### 3. Polyglot MCP Tools

Create MCP tools specifically designed for polyglot analysis:

- `analyze_polyglot`: Analyze multi-language project
- `detect_language_boundaries`: Find interop points between languages
- `cross_language_complexity`: Calculate complexity across language boundaries
- `polyglot_refactor`: Refactoring operations that work across languages

**Implementation files**:
- `server/src/mcp_integration/polyglot_tools.rs`
- `server/src/mcp_pmcp/polyglot_handlers.rs`

### 4. Performance Optimization

Implement performance enhancements for multi-language analysis:

- Parallel language analysis scheduler
- Language-specific caching strategies
- Incremental analysis for changed files only

**Implementation files**:
- `server/src/services/polyglot_analyzer.rs`
- `server/src/services/parallel_language_scheduler.rs`
- `server/src/services/incremental_analyzer.rs`

## Specific Use Cases

### JVM Interoperability Analysis

Focus on analyzing interoperability between JVM languages:

1. **Java-Scala Interop**:
   - Detect Scala use of Java classes and vice versa
   - Analyze implications of Java-Scala boundary crossing
   - Identify potential type system impedance mismatches

2. **Kotlin-Java Interop**:
   - Track nullable vs non-null type handling across boundaries
   - Detect Kotlin extension functions on Java classes
   - Analyze coroutine usage from Java code

3. **Scala-Kotlin Interop**:
   - Identify functional programming pattern differences
   - Track type inference challenges across language boundaries

### Web Application Stack Analysis

For web applications that typically use multiple languages:

1. **Frontend-Backend Analysis**:
   - Track TypeScript/JavaScript to Java/Kotlin/Scala endpoints
   - Analyze data model consistency across language boundaries
   - Detect API contract violations

2. **ORM/Database Layer**:
   - Analyze SQL queries generated by JVM ORM frameworks
   - Track database schema to language type mapping
   - Detect N+1 query problems across language boundaries

## Daily Plan

### Week 1

**Monday**: 
- Design unified polyglot AST representation
- Create initial test fixtures with multi-language examples

**Tuesday**:
- Implement cross-language dependency detector
- Create graph visualization for cross-language dependencies

**Wednesday**:
- Implement polyglot graph builder
- Begin work on unified AST representation

**Thursday**:
- Complete unified AST representation
- Create cross-language mapper interfaces

**Friday**:
- Begin implementation of polyglot MCP tools
- Integration testing for week 1 components

### Week 2

**Monday**: 
- Complete polyglot MCP tool implementation
- Begin performance optimization work

**Tuesday**:
- Implement parallel language analysis scheduler
- Add language-specific caching strategies

**Wednesday**:
- Implement incremental analysis support
- Create comprehensive test suite for all components

**Thursday**:
- Documentation and example creation
- Complete integration testing

**Friday**:
- Bug fixes and performance optimization
- Final review and sprint completion

## Metrics and Success Criteria

1. **Performance**:
   - Multi-language analysis should complete in under 2 seconds for projects under 10,000 LOC
   - Memory usage should not exceed 2x the single largest language analyzer

2. **Accuracy**:
   - Cross-language dependency detection should have >95% precision and recall
   - Unified AST representation should preserve all semantically significant information

3. **Coverage**:
   - Implementation should support all JVM languages (Java, Kotlin, Scala)
   - Initial support for at least one non-JVM language integration (e.g., TypeScript/JavaScript)

4. **Usability**:
   - All polyglot tools should be accessible via MCP interface
   - Documentation should include examples for common polyglot scenarios

## Dependencies and Risks

### Dependencies

- Requires successful implementation of Java and Scala support from Sprint 51
- Depends on unified AST framework from previous sprints

### Risks

1. **Complexity**: Cross-language analysis is inherently complex and may require more sophisticated algorithms than planned
   - Mitigation: Start with well-defined subset of features and expand incrementally

2. **Performance**: Analyzing multiple languages simultaneously may cause performance issues
   - Mitigation: Implement early performance testing and parallel processing

3. **Scope Creep**: The breadth of potential polyglot analysis is vast
   - Mitigation: Clearly define specific use cases and limit initial implementation

## Resources Required

1. **Personnel**:
   - 2 senior engineers familiar with JVM ecosystem
   - 1 engineer experienced with TypeScript/JavaScript
   - 1 quality engineer for testing

2. **Infrastructure**:
   - CI/CD pipeline with multi-language support
   - Test environments for various language combinations

3. **External Dependencies**:
   - Sample polyglot applications for testing
   - Documentation on language interoperability standards

## Conclusion

Sprint 52 will significantly enhance PMAT's ability to analyze polyglot projects by implementing cross-language analysis capabilities. The focus on JVM language interoperability builds directly on the work from Sprints 50-51 while extending PMAT's capabilities to handle the reality of modern polyglot software development.

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