SLICEFORMER: Static Program Slicing Using Language Models With Dataflow-Aware Pretraining and Constrained Decoding

ACL ARR 2026 January Submission6405 Authors

Keywords: static program slicing, pre-training, constraint decoding, language model.

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.

Paper Type: Long

Research Area: Code Models

Research Area Keywords: Code Models, Language Modeling, Semantics: Lexical and Sentence-Level

Contribution Types: Publicly available software and/or pre-trained models

Languages Studied: Python, Java

Submission Number: 6405