Static Program Slicing Using Language Models With Dataflow-Aware Pretraining and Constrained Decoding
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+. Sliceformer introduces two key innovations that directly target the identified...
| Authors | Pengfei He, Shaowei Wang, Tse-Hsun Chen et al. |
|---|---|
| Primary category | cs.SE |
| Published | 9 April 2026 |
| Last updated at source | 10 May 2026 |
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