Agentic data science with the Data Agent Kit
cloud.google.com·7h ago
AI Research
arXiv CS.AI·Aug 26·5 min read
Identifying Latent Declarative Representations of Code for Assisting Repository Migration
TL;DR
ADFD-Migrate is a tool that enhances the migration of legacy code by making latent declarative representations explicit, achieving an 85.6% success rate in porting Fortran repositories to Python.
✦ Why It Matters
Consider using ADFD-Migrate for your legacy code migration projects to improve accuracy and efficiency.
Key Takeaways
- Utilize ADFD-Migrate for effective legacy code migration.
- Leverage annotated data-flow diagrams for better understanding.
- Evaluate migration success with behavioral probes.
- Aim for high migration outcome indices to ensure quality.
How It Works
ADFD-Migrate creates an annotated data-flow diagram (ADFD) that represents the latent declarative description of a program. It uses a large language model (LLM) to infer this representation from the existing codebase, ensuring that all relevant processes and data flows are captured.
By comparing the inferred source ADFD with a target ADFD, the tool identifies discrepancies that guide the regeneration of code in the target language, enhancing the accuracy of the migration.