mlsanityData to 14 Oct 2026

Specification Portability Across LLM Development Agents: Cross-Agent Compatibility in Specification-Driven Software Migration

arXiv 2608.21208 ↗First posted 21 Aug 2026cs.SE cs.AI cs.LOcited by 0

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Abstract (arXiv)

This paper investigates cross-agent specification portability using Oracle-to-PostgreSQL migration as a controlled software transformation task. The study combines two experimental stages. First, a specification-first migration pipeline was evaluated on 1,006 PL/SQL files, of which 623 were successfully regenerated and 380 generated scripts executed successfully in PostgreSQL 16. Second, cross-agent experiments were conducted on a dataset of 1,802 Oracle scripts with corresponding PostgreSQL implementations using Amazon Kiro, Google Gemini, and GitHub Copilot, with Claude Code and Cursor included in the initial single-agent evaluation. Native and foreign specifications were assessed using Token F1, exact match, SQL syntax validity, AST exact match, AST mean similarity, and immediate runnability. The results show that specification size alone does not predict implementation quality and that cross-agent transfer can produce substantial agent-dependent degradation. The strongest replicated case occurred when Gemini directly consumed a Kiro-origin specification, producing a Token F1 of 0.035, SQL syntax validity of 2.33%, and AST mean similarity of 0.015. Rewriting substantially improved Gemini in the tested configuration, compression did not provide a universal benefit, and retrieval-augmented ingestion was the only common strategy represented on the per-agent Pareto frontiers of both Gemini and Copilot. The findings suggest that specifications in heterogeneous SDD workflows should not automatically be treated as agent-neutral artifacts and motivate explicit consideration of specification portability, agent-specific interpretation, and retrieval-based access in multi-agent software engineering.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 18 of its 27 references that are in the map. First posted on arXiv 21 Aug 2026.

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18 of 27 references.

References · 18

  1. 2023SWE-bench: Can Language Models Resolve Real-World GitHub Issues?cited by 3,373
  2. 2023MetaGPT: Meta Programming for A Multi-Agent Collaborative Frameworkcited by 1,808
  3. 2023Is Your Code Generated by ChatGPT Really Correct? Rigorous Evaluation of Large Language Models for Code Generationcited by 1,391
  4. 2023ChatDev: Communicative Agents for Software Developmentcited by 919
  5. 2023Large Language Models for Software Engineering: A Systematic Literature Reviewcited by 399
  6. 2023RepoCoder: Repository-Level Code Completion Through Iterative Retrieval and Generationcited by 367
  7. 2023CrossCodeEval: A Diverse and Multilingual Benchmark for Cross-File Code Completioncited by 183
  8. 2025AI Agents vs. Agentic AI: A Conceptual Taxonomy, Applications and Challengescited by 169
  9. 2023An Empirical Study of the Non-determinism of ChatGPT in Code Generationcited by 137
  10. 2024DevEval: A Manually-Annotated Code Generation Benchmark Aligned with Real-World Code Repositoriescited by 72
  11. 2024Repoformer: Selective Retrieval for Repository-Level Code Completioncited by 55
  12. 2025Retrieval-Augmented Code Generation: A Survey with Focus on Repository-Level Approachescited by 23
6 more
  1. 2026Spec-Driven Development:From Code to Contract in the Age of AI Coding Assistantscited by 14
  2. 2026Specification-Based Code-Text-Code Reengineering for LLM-Mediated Software Evolutioncited by 5
  3. 2025CrackSQL: A Hybrid SQL Dialect Translation System Powered by Large Language Modelscited by 4
  4. 2026Unified Architecture Metamodel of Information Systems Developed by Generative AIcited by 4
  5. 2026Token Optimization Strategies for LLM-Based Oracle-to-PostgreSQL Migrationcited by 3
  6. 2026Specification-Driven Development Benchmark: Security Knowledge Transitioncited by 2

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  4. 2025Agentic Specification Generator for Move Programs
  5. 2026Toward Safe LLM Agents: A Survey of Specification, Verification, and Enforcement
  6. 2026LegacyTranslate: LLM-based Multi-Agent Method for Legacy Code Translation
  7. 2026Schema First Tool APIs for LLM Agents: A Controlled Study of Tool Misuse, Recovery, and Budgeted Performance
  8. 2025LLM Agents for Generating Microservice-based Applications: how complex is your specification?