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arXiv · 2506.02073

Flow2Code: Evaluating Large Language Models for Flowchart-based Code Generation Capability

Mengliang He·Jiayi Zeng·Yankai Jiang·Wei Zhang·Zeming Liu·Xiaoming Shi·Aimin Zhou
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Abstract

While large language models (LLMs) show promise in code generation, existing benchmarks neglect the flowchart-based code generation. To promote further research on flowchart-based code generation, this work presents Flow2Code, a novel benchmark for flowchart-based code generation evaluation. The evaluation dataset spans 15 programming languages and includes 5,622 code segments paired with 16,866 flowcharts of three types: code, UML, and pseudocode. Extensive experiments with 13 multimodal LLMs reveal that current LLMs can not generate code based on flowcharts perfectly. Besides, experiment results show that the supervised fine-tuning technique contributes greatly to the models' performance. We publicly release our code and datasets at https://github.com/hml-github/Flow2Code.

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Mengliang He, Jiayi Zeng, Yankai Jiang, Wei Zhang, Zeming Liu, Xiaoming Shi, Aimin Zhou. 2025-06-02. Flow2Code: Evaluating Large Language Models for Flowchart-based Code Generation Capability. https://arxiv.org/abs/2506.02073

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