AgenticMigration: A Human-AI Collaborative Scientific Code Migration Workflow

Project Basic Info

• Original Author/Maintainer: ssciwr (Scientific Software Center, Heidelberg University)
• Source Platform: GitHub
• Release Date: May 29, 2026

Core Content

AgenticMigration is a human-AI collaborative code migration workflow for the scientific computing domain, specifically addressing migration issues of legacy code such as Fortran, C++, and old Python versions. Its core concept is "human-in-the-loop": AI handles repetitive migration tasks, while human experts are responsible for key decisions and quality control, balancing efficiency and quality, and enabling knowledge transfer and incremental migration.

Challenges of Legacy Code

Historical code in the scientific computing domain (Fortran, C++, early Python) faces:

• Difficulty leveraging modern resources due to hardware architecture evolution
• Security risks from discontinued dependency libraries
• Maintenance difficulties caused by original developers' departure

Limitations of Traditional Migration

• Manual Rewriting: High cost, requiring scarce talent proficient in both old and new technologies
• Fully Automated Tools: Struggle to handle the complexity and domain-specificity of scientific code

AgenticMigration addresses this pain point by proposing a human-AI collaborative solution between fully manual and fully automated approaches.

Core Concept: Human-in-the-Loop

Advantages include:

• Balance of efficiency and quality: AI handles standardized conversions; humans ensure correct migration of domain knowledge
• Risk control: Key decisions are reviewed by humans
• Knowledge transfer: Migration process is documented
• Incremental progress: Migrate in modules and phases

Multi-Stage Migration Pipeline

1. Code Analysis and Understanding: Build code structure graphs, identify dependencies, data flows, and key algorithms
2. Migration Strategy Planning: AI generates proposal suggestions; humans review and adjust
3. Code Conversion and Refactoring: Syntax conversion, data structure modernization, test generation, etc.
4. Validation and Testing: Unit test comparison, performance benchmarking, numerical precision analysis
5. Manual Review and Iteration: Humans mark improvement points; AI iterates and optimizes

Fortran Code Modernization

• Fixed-form → free-form conversion
• Migration from old to modern Fortran standards
• Cross-language migration from Fortran to Python (NumPy/SciPy)
• OpenMP old syntax → new standard parallelization

C++ Code Refactoring

• C++98/03 → C++17/20/23 standard upgrade
• Raw pointers → smart pointers conversion
• Simplification of template metaprogramming
• Dependency library update and replacement

Python 2→3 Upgrade

• Syntax fixes (print statements, division, etc.)
• Standardization of string/byte processing
• Dependency library compatibility handling
• Integration of type annotation addition and checking

Digital Transformation of Research Institutions

• Protect research investments and avoid redundant development
• Adapt historical achievements to modern computing platforms
• Lower the threshold for young researchers to take over legacy code
• Promote open-source code collaboration

Industrial Simulation Software Maintenance

• Extend the lifecycle of core algorithms
• Support migration to GPU/distributed computing
• Improve integration with modern tools
• Meet maintainability compliance requirements

Open Source Scientific Computing Ecosystem Construction

• Give new life to classic algorithm libraries
• Lower the threshold for new contributors
• Promote cross-language tool integration

Semantic Equivalence Guarantee

• Symbolic execution to compare key function logic
• Fuzz testing to generate large amounts of input for output validation
• Domain experts annotate precision-sensitive paths
• Layered incremental validation

Performance Regression Prevention

• Performance profiling to guide optimization
• JIT compilation and vectorization suggestions
• Preserve C/Fortran in critical paths
• Redesign parallelization schemes

Dependency Management Complexity

• Dependency graph visualization
• Alternative library recommendations
• Containerized deployment solutions
• Automatic virtual environment configuration

Open Source Model Value

• Joint improvement by the global scientific research community
• Plugin mechanism supports discipline-specific needs
• Best practices spread quickly
• Lower the threshold for small teams to modernize code

Project Significance

AgenticMigration demonstrates a new paradigm for human-AI collaboration to solve complex software engineering problems. As AI capabilities improve, this "AI handles patterned tasks + human decision-making" model will be applied in more fields.