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arXiv · 2505.01475

CodeSSM: Towards State Space Models for Code Understanding

Shweta Verma·Abhinav Anand·Mira Mezini
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Abstract

Although transformers dominate many code-specific tasks, they have significant limitations. This paper explores State Space Models (SSMs) as a promising alternative for code understanding tasks such as retrieval, classification, and clone detection. We introduce CodeSSM, the first SSM-based model trained on code corpora to assess its effectiveness. Our results demonstrate that SSMs are more sample-efficient and can extrapolate to longer contexts beyond the pretraining length. Extensive experiments show that SSMs offer a viable alternative to transformers, addressing several their limitations. Additionally, CodeSSM reduces memory usage by up to 64\% compared to transformers at a context length of 2048, with greater savings as context length grows.

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Shweta Verma, Abhinav Anand, Mira Mezini. 2025-05-02. CodeSSM: Towards State Space Models for Code Understanding. https://arxiv.org/abs/2505.01475

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