Automating Java Code Fixes in WCA4z with Agentic AI

Business Challenge

Current Limitation:
The WCA4z tool transforms legacy code into Java. However, the transformed Java code often contains syntax or semantic errors that require significant manual intervention to fix. This process is time-consuming, error-prone, and demands deep technical expertise, slowing down modernization efforts and increasing the cost of transformation.

Opportunity for Improvement:
By integrating Agentic AI into the WCA4z transformation pipeline, we can automate the error detection and correction process. AI agents can analyze the transformed code, identify issues, and apply intelligent fixes based on learned patterns and best practices.

Potential Benefits:

Faster time-to-market for modernized applications

Reduced manual effort and cost associated with code correction

Improved code quality and consistency

Increased productivity for development and QA teams

This challenge aims to explore how Agentic AI can be used to streamline the transformation process, reduce human error, and accelerate IBM’s modernization initiatives.

Technical Proposal

Title: Agentic AI for Automated Java Code Correction in WCA4z

We propose a multi-agent AI system that integrates with the WCA4z transformation process to automatically detect and fix errors in the generated Java code. The system will consist of specialized agents, each responsible for a distinct aspect of code analysis and correction.

Key Components:

Code Analyzer Agent
Scans the transformed Java code for syntax errors, deprecated patterns, and semantic inconsistencies using static analysis and LLM-based code understanding.

Error Classifier Agent
Categorizes errors into fixable patterns (e.g., missing imports, incorrect method signatures, type mismatches) and flags complex issues for human review.

Fix Generator Agent
Uses fine-tuned LLMs or retrieval-augmented generation (RAG) to suggest and apply context-aware fixes, referencing IBM’s internal codebases and transformation rules.

Regression Validator Agent
Runs unit tests or compiles the fixed code to ensure correctness and prevent regressions.

Feedback Loop Agent
Captures developer feedback on AI-generated fixes to continuously improve the model’s accuracy and reliability.

This agentic system can be deployed as a post-processing step in the WCA4z pipeline or integrated into an IDE plugin for real-time assistance. It will significantly reduce the manual burden on developers, improve transformation quality, and accelerate the modernization journey.