LLM Specification Portability in Software Migration
Listen to the summary
Uses a voice available on your device
Audio options
On this page 4 sections
Related concepts 2 concepts
Key Takeaways
- Initial migration efforts successfully regenerated 623 out of 1,006 PL/SQL files, with 380 producing scripts that executed successfully in PostgreSQL 16.
- Cross-agent transfer can cause significant performance degradation that varies based on the specific agent being used.
- Specifications should not be automatically treated as agent-neutral in heterogeneous software development workflows.
- Specification size does not serve as a reliable predictor of the resulting implementation quality.
Summary & Methodology Analysis
The study utilized a migration pipeline designed for specification-first development to evaluate portability across multiple LLM development agents. The pipeline was tested against a dataset of 1,802 Oracle scripts and their associated PostgreSQL implementations. In the primary evaluation, researchers used Amazon Kiro, Google Gemini, GitHub Copilot, Claude Code, and Cursor to assess migration success through metrics such as Token F1, SQL syntax validity, and AST mean similarity.
Interactive System Flowchart
Cross-Examination & FAQs
A deeper dive clarifying mechanics, constraints, and baseline evaluations.
Q1. What is the main finding regarding LLM agents and migration specifications?
Specifications in heterogeneous software development workflows should not be assumed to be agent-neutral.
Q2. Did the size of the specification predict the quality of the migration?
No, the results show that specification size alone does not predict implementation quality.
Q3. Are migration specifications interchangeable between different LLM agents?
No, cross-agent transfer can produce substantial agent-dependent degradation.
Q4. What performance metrics were observed when Gemini consumed a Kiro-origin specification?
It produced a Token F1 of 0.035, SQL syntax validity of 2.33%, and AST mean similarity of 0.015.
Q5. What was the success rate in the initial migration pipeline evaluation?
623 out of 1,006 PL/SQL files were successfully regenerated, with 380 resulting in scripts that executed successfully in PostgreSQL 16.
Q6. Which models were evaluated in the cross-agent experiments?
Amazon Kiro, Google Gemini, and GitHub Copilot were used for cross-agent experiments, while Claude Code and Cursor were included in the initial single-agent evaluation.
Q7. How many scripts were used in the cross-agent experiments?
The researchers used a dataset of 1,802 Oracle scripts with corresponding PostgreSQL implementations.
Q8. Does the paper specify the hardware requirements for these agents?
The paper does not specify hardware requirements.
Q9. What specific metrics did the study use to assess performance?
The paper used Token F1, SQL syntax validity, and AST mean similarity.