@inproceedings{verma-etal-2025-codessm,
    title = "{C}ode{SSM}: Towards State Space Models for Code Understanding",
    author = "Verma, Shweta  and
      Anand, Abhinav  and
      Mezini, Mira",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-main.1735/",
    pages = "34207--34223",
    ISBN = "979-8-89176-332-6",
    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.The code is available [here](https://github.com/abx04/CodeSSM)."
}