CrossPL: Evaluating Large Language Models on Cross Programming Language Code Generation

Zhanhang Xiong; Dongxia Wang; Yuekang Li; Xinyuan An; Wenhai Wang

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arXiv · 2507.19904

CrossPL: Evaluating Large Language Models on Cross Programming Language Code Generation

Zhanhang Xiong·Dongxia Wang·Yuekang Li·Xinyuan An·Wenhai Wang

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Abstract

As large language models (LLMs) become increasingly embedded in software engineering workflows, a critical capability remains underexplored: generating correct code that enables cross-programming-language (CPL) interoperability. This skill is essential for building complex systems that integrate components written in multiple languages via mechanisms like inter-process communication (IPC). To bridge this gap, we present CrossPL, the first benchmark designed to systematically evaluate LLMs' ability to generate CPL-interoperating code. CrossPL comprises 1,982 tasks centered around IPC, covering six widely-used programming languages and seven representative CPL techniques. We construct this benchmark by (i) analyzing 19,169 multi-language GitHub repositories using 156 hand-crafted finite state machines (FSMs), and (ii) developing an LLM-based pipeline that automatically extracts CPL code snippets, generates task instructions, and validates functional correctness. We evaluate 14 state-of-the-art general-purpose LLMs and 6 code-oriented LLMs released in the past three years on CrossPL via FSM-based validation. Results reveal that even the best-performing models struggle with CPL scenarios, underscoring the need for more targeted research in this space. Our benchmark and code are available at: https://anonymous.4open.science/r/crosspl-2814.

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Zhanhang Xiong, Dongxia Wang, Yuekang Li, Xinyuan An, Wenhai Wang. 2025-07-26. CrossPL: Evaluating Large Language Models on Cross Programming Language Code Generation. https://arxiv.org/abs/2507.19904

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