# ExeCoder: Empowering Large Language Models with Executability Representation for Code Translation

arXiv: https://arxiv.org/abs/2501.18460 · HTML: https://mlsanity.com/paper/2501.18460/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jan 2025.

## Built on by (most cited first, 10 of 10)

- [ARCS: Agentic Retrieval-Augmented Code Synthesis with Iterative Refinement](https://mlsanity.com/paper/2504.20434/) (2025)
- [Large Language Models as Computable Approximations to Solomonoff Induction](https://mlsanity.com/paper/2505.15784/) (2025)
- [Detection of LLM-Paraphrased Code and Identification of the Responsible LLM Using Coding Style Features](https://mlsanity.com/paper/2502.17749/) (2025)
- [WarriorMath: Enhancing the Mathematical Ability of Large Language Models with a Defect-aware Framework](https://mlsanity.com/paper/2508.01245/) (2025)
- [BabelCoder: Agentic Code Translation with Specification Alignment](https://mlsanity.com/paper/2512.06902/) (2025)
- [Automated Snippet-Alignment Data Augmentation for Code Translation](https://mlsanity.com/paper/2510.15004/) (2025)
- [CangjieBench: Benchmarking LLMs on a Low-Resource General-Purpose Programming Language](https://mlsanity.com/paper/2603.14501/) (2026)
- [Converted, Not Equivalent: Benchmarking Codebase Conversion via Observational Equivalence](https://mlsanity.com/paper/2605.29054/) (2026)
- [Bridging Functional Correctness and Runtime Efficiency Gaps in LLM-Based Code Translation](https://mlsanity.com/paper/2606.17683/) (2026)
- [Bootstrapping Niche Multilingual Code Translation via Reinforcement Learning with Execution-Based Verifiable Supervision](https://mlsanity.com/paper/2608.13854/) (2026)

## Abstract (arXiv)

Code translation is a crucial activity in the software development and maintenance process, and researchers have recently begun to focus on using pre-trained large language models (LLMs) for code translation. However, existing LLMs only learn the contextual semantics of code during pre-training, neglecting executability information closely related to the execution state of the code, which results in unguaranteed code executability and unreliable automated code translation. To address this issue, we propose ExeCoder, an LLM specifically designed for code translation, aimed at utilizing executability representations such as functional semantics, syntax structures, and variable dependencies to enhance the capabilities of LLMs in code translation. To evaluate the effectiveness of ExeCoder, we manually enhanced the widely used benchmark TransCoder-test, resulting in a benchmark called TransCoder-test-X that serves LLMs. Evaluation of TransCoder-test-X indicates that ExeCoder achieves state-of-the-art performance in code translation, surpassing existing open-source code LLMs by over 10.88% to 38.78% and over 27.44% to 42.97% on two metrics, and even outperforms the renowned closed-source LLM GPT-4o. Code is available at https://aka.ms/execoder