@inproceedings{he-etal-2026-sliceformer,
    title = "{SLICEFORMER}: Static Program Slicing Using Language Models With Dataflow-Aware Pretraining and Constrained Decoding",
    author = "He, Pengfei  and
      Wang, Shaowei  and
      Chen, Tse-Hsun  and
      Asaduzzaman, Muhammad",
    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.1649/",
    pages = "35641--35652",
    ISBN = "979-8-89176-390-6",
    abstract = "Static program slicing is a fundamental software engineering technique for isolating code relevant to specific variables. While recent learning-based approaches using language models (LMs) show promise in automating slice prediction, they suffer from inaccurate dependency modeling and unconstrained generation, where LMs fail to capture precise data flow relations and produce slices containing hallucinated tokens and statements. To address these challenges, we propose SliceFormer, a novel approach that reformulates static program slicing as a sequence-to-sequence task using small language models such as CodeT5+. introduces two key innovations that directly target the identified limitations. First, to improve dependency modeling, we design dataflow-aware pretraining objectives that leverage data flow graphs DFG to teach models data dependencies through dataflow-preserving statement permutation and dataflow-aware span corruption. Second, to eliminate hallucination, we develop a constrained decoding mechanism that enforces both lexical and syntactic constraints. We evaluate SliceFormer on Java and Python program slicing benchmarks, demonstrating consistent improvements over state-of-the-art baselines with up to 22{\%} gain in ExactMatch."
}