Bridging Functional Correctness and Runtime Efficiency Gaps in LLM-Based Code Translation

2026-06-16

AI Summary

This work addresses the persistent gap in runtime efficiency between code translations generated by large language models (LLMs) and those written by humans—a limitation that is difficult to mitigate through prompt engineering alone. To bridge this gap, the authors propose SwiftTrans, a novel framework that first generates diverse translation candidates through multi-perspective exploration and then selects the optimal solution using a discrepancy-aware selection mechanism. The framework further incorporates hierarchical and ordinal guidance strategies to enhance performance. Notably, this study is the first to systematically balance both functional correctness and runtime efficiency in LLM-based code translation. The authors introduce SwiftBench, a new benchmark tailored for this dual objective, and demonstrate that SwiftTrans achieves consistent and significant improvements over existing methods across CodeNet, F2SBench, and SwiftBench.

Abstract

While large language models (LLMs) have greatly advanced the functional correctness of automated code translation systems, the runtime efficiency of translated programs has received comparatively little attention. With the waning of Moore's law, runtime efficiency has become increasingly important for program quality, alongside functional correctness. Our preliminary study reveals that LLM-translated programs often run slower than human-written ones, and this issue cannot be remedied through prompt engineering alone. Therefore, our work proposes SwiftTrans, a code translation framework comprising two key stages: (1) Multi-Perspective Exploration, where MpTranslator leverages parallel in-context learning (ICL) to generate diverse translation candidates; and (2) Difference-Aware Selection, where DiffSelector identifies the optimal candidate by explicitly comparing differences between translations. We further introduce Hierarchical Guidance for MpTranslator and Ordinal Guidance for DiffSelector, enabling LLMs to better adapt to these two core components. To support the evaluation of runtime efficiency in translated programs, we extend existing benchmarks, CodeNet and F2SBench, and introduce a new benchmark, SwiftBench. Experimental results across all three benchmarks show that SwiftTrans achieves consistent improvements in both correctness and runtime efficiency.

Problem

• code translation
• runtime efficiency
• functional correctness
• large language models
• program performance

Innovation

• code translation
• runtime efficiency
• large language models
• in-context learning
• benchmarking