P-Slicer: A Program Slicing Approach Based on Learning Path Representations

LIU Tian-yang; SHI Jian-jun; YE Jia-wei; JI Wei-xing

doi:10.12263/DZXB.20250824

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P-Slicer: A Program Slicing Approach Based on Learning Path Representations

PAPERS | 更新时间：2026-02-10

- P-Slicer: A Program Slicing Approach Based on Learning Path Representations
- ACTA ELECTRONICA SINICA Vol. 53, Issue 11, Pages: 3894-3909(2025)
- 作者机构：
  
  1.北京理工大学计算机学院，北京 100081
  2.北京师范大学人工智能学院，北京 100875
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China(62232003)
- DOI：10.12263/DZXB.20250824
  CLC： TP311;
- Received：22 September 2025，
  
  Accepted：04 November 2025，
  
  Published：25 November 2025
- 稿件说明：
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刘天阳, 石剑君, 叶嘉威, 等. P-Slicer：面向路径表示学习的程序切片方法[J]. 电子学报, 2025, 53(11): 3894-3909.

LIU Tian-yang, SHI Jian-jun, YE Jia-wei, et al. P-Slicer: A Program Slicing Approach Based on Learning Path Representations[J]. Acta Electronica Sinica, 2025, 53(11): 3894-3909.
刘天阳, 石剑君, 叶嘉威, 等. P-Slicer：面向路径表示学习的程序切片方法[J]. 电子学报, 2025, 53(11): 3894-3909. DOI：10.12263/DZXB.20250824

LIU Tian-yang, SHI Jian-jun, YE Jia-wei, et al. P-Slicer: A Program Slicing Approach Based on Learning Path Representations[J]. Acta Electronica Sinica, 2025, 53(11): 3894-3909. DOI：10.12263/DZXB.20250824

摘要

程序切片技术作为软件分析中的基础性手段，在程序理解、缺陷定位、代码重构等任务中具有重要作用.其核心挑战在于如何在复杂控制流和数据流结构中准确识别与切片准则相关的代码片段.近年来，基于预训练大语言模型的切片方法因其对程序语义建模能力较强而展现出良好性能，然而受限于模型输入长度限制，难以有效处理长方法体及跨过程依赖等实际场景.针对以上问题，本文提出一种面向路径表示学习的程序切片方法P-Slicer.该方法首先通过构建基于语法结构的控制流图，从中提取多条可能的执行路径，以实现高代码覆盖率并保留上下文信息；随后，采用基于学习的分类模型对方法内部语句进行切片相关性判断；最后，结合变量的定义-使用传播机制，实现跨过程切片的递归分析.该方法在保持可扩展性的同时，融合了语义理解能力，提升了切片结果的准确性与实用性.实验结果表明，P-Slicer在切片任务中取得了95.95%的准确率、86.89%精确度和88.95%的召回率，且在处理长方法和跨过程切片时仍能保持良好性能，表明其在软件工程领域中的良好应用前景.

Abstract

Program slicing is a foundational technique in software analysis

indispensable for tasks such as program understanding

defect localization

and code refactoring. Its primary challenge is to precisely identify code fragments related to a given slicing criterion within complex control and data flow structures. Recently

program slicing approaches based on pre-trained large language models have shown promising results

owing to their strong capability in capturing program semantics. However

due to the model’s limitation on input length

it is difficult to handle practical scenarios such as long methods and interprocedural dependencies. To address these problems

this paper proposes P-Slicer

a program slicing approach based on learning path representations. This approach first extracts multiple execution paths by building a control flow graph based on the syntactic structure to achieve high code coverage while preserving contextual information. Then

a learning-based classification model is employed to determine the relevance of each statement to the slice criterion. Finally

a variable define-use propagation mechanism for variables is employed to achieve interprocedural slices by recursive analysis. The approach integrates semantic comprehension while preserving the scalability

thereby enhancing the accuracy and practicality of the slicing results. The experimental results demonstrate that P-Slicer achieves 95.95% accuracy

86.89% precision

and 88.95% recall on slicing task

while maintaining robust performance when handling long methods and interprocedural slices

indicating its promising potential for application in the software engineering.

关键词

Keywords

references

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