Code Translation
TransAgent: Enhancing LLM-Based Code Translation via Fine-Grained Execution Alignment
Code translation transforms code between programming languages while preserving functionality, crucial for software development. Traditional methods struggle with data scarcity, but LLMs show promise. However, LLM-translated code often has syntax and semantic errors. This work proposes TransAgent, a multi-agent system, to fix these errors by localizing error-prone code blocks through fine-grained execution alignment between source and target code.
Executive Impact: At a Glance
TransAgent significantly outperforms state-of-the-art methods like TRANSCODER (87.1% higher CA) and UNITRANS (33.3% higher CA) in translation accuracy. It achieves an average improvement of 56.7% over AGENTLESS in program repair. The system generalizes effectively across various LLMs and maintains stable performance even with increasing code complexity. Its block-level mapping and runtime-guided fault localization are key innovations.
33.3%
Accuracy Gain vs. UNITRANS
56.7%
Repair Improvement vs. AGENTLESS
95.8%
Block Mapping Accuracy
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Code Translation
TransAgent's Multi-Agent Workflow
TransAgent employs four collaborative agents to achieve robust code translation. The process starts with initial translation, moves to syntax fixing, then code alignment, and finally semantic error correction.
Initial Code Translation
→
Syntax Error Fixing
→
Code Alignment (CFG & LLM)
→
Semantic Error Fixing
Unrivaled Translation Accuracy
TransAgent achieves a remarkable 89.5% Computational Accuracy (CA) in Python-to-Java translation, significantly outperforming baselines and setting a new standard for LLM-based code translation.
89.5% Python-to-Java CAWhy TransAgent Outperforms Baselines
A side-by-side comparison highlights TransAgent's unique strengths in code translation and error repair.
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Case Study: Fixing a Semantic Error in Java
In one instance, a Java program translated from Python missed a key deduplication, leading to incorrect output. UNITRANS failed to identify the precise error. TransAgent, however, pinpointed the error to a specific line (Line 3) by comparing intermediate runtime values, providing focused hints for LLMs to resolve the issue successfully.
Language: Java (translated from Python)
Problem: Missed deduplication operation, incorrect output (0 instead of 4).
Baseline Failure: UNITRANS failed due to program complexity and lack of fine-grained error localization.
TransAgent Solution: Localized error to Line 3 by comparing runtime values between Python source and Java target, providing focused hints for LLM to apply a correct patch.
Outcome: Successful semantic error correction.
Advanced ROI Calculator
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Estimated Annual Savings
Hours Reclaimed Annually
Implementation Roadmap
A phased approach to integrate TransAgent into your existing software development lifecycle.
Phase 1: Discovery & Integration
Initial assessment of existing codebases and setup of TransAgent's multi-agent environment, including LLM integration and control flow analysis tools.
Phase 2: Pilot Program & Evaluation
Run TransAgent on a selection of projects for code translation and error repair, closely monitoring performance and refining configurations.
Phase 3: Rollout & Optimization
Full-scale deployment across translation tasks, continuous monitoring, and iterative improvements based on real-world performance metrics.
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