@inproceedings{gong-etal-2026-trace,
    title = "{TRACE}: Evaluating Execution Efficiency of {LLM}-Based Code Translation",
    author = "Gong, Zhihao  and
      Sun, Zeyu  and
      Huang, Dong  and
      Liang, Qingyuan  and
      Zhang, Jie M.  and
      Hao, Dan",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-long.140/",
    pages = "3089--3117",
    ISBN = "979-8-89176-390-6",
    abstract = "While Large Language Models (LLMs) have substantially improved the functional correctness of code translation, the critical dimension of execution efficiency remains overlooked. We present \textbf{Trace}, the first benchmark to explicitly assess efficiency in LLM-translated code. Trace includes 1,000 efficiency-critical tasks across C++, Java, and Python, each augmented with stress tests that reveal efficiency disparities often overlooked by small-scale tests. Using Trace, we conduct an extensive evaluation of 28 representative LLMs and highlight several key insights: 1) Correctness and efficiency are often misaligned: the correctness leader Claude-Sonnet-4-Think achieves only moderate time efficiency, outperformed by smaller open-source LLMs such as Qwen2.5-Coder-14B-Instruct. 2) Inefficiency is both prevalent and patterned: 23.5{\%} of correct translations suffer from notable inefficiency, mainly arising from algorithm implementation discrepancy (11.9{\%}), language construct mismatch (66.4{\%}), and resource management inefficiency (21.7{\%}).3) Inference-time prompt strategies bring only modest improvements, indicating that simple prompting alone is insufficient to improve translation efficiency. Together, our results establish execution efficiency as an essential dimension of code translation and position Trace as a principled foundation for efficiency-oriented evaluation. Our code and data are available at: \url{https://github.com/Albert-Gong/TRACE}."
}