.png){kind=icon}
.png){kind=icon}
Return
Interactive LLM-Driven Framework for Cross-Architecture Code Migration: Balancing Efficiency, Accuracy, and Explainability
.png){kind=icon}
PRE
AI
DOI：10.1002/cpe.70705.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Cross-architecture code migration has become essential as data centers transition from homogeneous x86 systems to heterogeneous one-cloud, multi-chip infrastructures that include ARM64, RISC-V, and domestic processors. Traditional approaches, such as manual refactoring and rule-based rewriting, face challenges in maintaining semantic correctness, scalability, and explainability. This paper presents an interactive, question-answering (QA)-driven framework that uses a frozen large language model (LLM) as a semantic reasoning engine. The framework combines static analysis, AST-based semantic-distance modeling, and a self-evolving rule base for validated transformations, ensuring transparency, explainability, and architecture-aware migration. It supports multiple languages (C++, Python, Java) and ISAs (x86, ARM64, RISC-V) without the need for fine-tuning the LLM. Evaluation on 30 real-world codebases shows an average migration accuracy of 92.4%, approaching expert-level performance (95.1%), with a 4.2 & times; reduction in migration errors and significant reduction in developer time (from 20-25 h to 8 h). These results demonstrate that the QA-driven, LLM-based migration framework significantly improves efficiency, accuracy, and scalability.
Keywords:
cross-architecture code migration
explainable software transformation
large language models
multi-ISA optimization
QA-driven interaction
Journal
C
IF:
1.5
Papers:
473
Citations:
0