一句话：提出 NS-Slicer，用预训练语言模型预测完整与不完整代码的静态切片，并用于漏洞检测。
传统程序切片技术对不完整代码无能为力，难以用于在线代码片段的早期缺陷检测与调试。为此，本文提出 NS-Slicer，一种基于学习的静态程序切片方法。它利用预训练语言模型对源码中细粒度变量-语句依赖关系的理解，给定变量位置和语句，判断该语句属于后向切片还是前向切片。在完整代码上，后向与前向切片的 F1 分别为 97.41% 和 95.82%，总体 F1 为 96.77%，其中 85.20% 的预测切片与 oracle 完全一致。在部分程序上，后向切片 F1 为 96.77%–97.49%，前向切片为 92.14%–95.40%，总体为 94.66%–96.62%。作者还将预测切片集成到自动漏洞检测工具中，在 Java 代码上取得 73.38% 的 F1，并分析了变量别名等代码内在属性对切片的影响。
Traditional program slicing techniques are crucial for early bug detection and manual/automated debugging of online code snippets. Nevertheless, their inability to handle incomplete code hinders their real-world applicability in such scenarios. To overcome these challenges, we present NS-Slicer, a novel learning-based approach that predicts static program slices for both complete and partial code Our tool leverages a pre-trained language model to exploit its understanding of fine-grained variable-statement dependencies within source code. With this knowledge, given a variable at a specific location and a statement in a code snippet, NS-Slicer determines whether the statement belongs to the backward slice or forward slice, respectively. We conducted a series of experiments to evaluate NS-Slicer's performance. On complete code, it predicts the backward and forward slices with an F1-score of 97.41% and 95.82%, respectively, while achieving an overall F1-score of 96.77%. Notably, in 85.20% of the cases, the static program slices predicted by NS-Slicer exactly match entire slices from the oracle. For partial programs, it achieved an F1-score of 96.77%–97.49% for backward slicing, 92.14%–95.40% for forward slicing, and an overall F1-score of 94.66%–96.62%. Furthermore, we demonstrate NS-Slicer's utility in vulnerability detection (VD), integrating its predicted slices into an automated VD tool. In this setup, the tool detected vulnerabilities in Java code with a high F1-score of 73.38%. We also include the analyses studying NS-Slicer’s promising performance and limitations, providing insights into its understanding of intrinsic code properties such as variable aliasing, leading to better slicing.
@article{paperbot2565,
title = {A Learning-Based Approach to Static Program Slicing},
author = {Aashish Yadavally and Yi Li and Shaohua Wang and Tien N. Nguyen},
journal = {Proceedings of the ACM on Programming Languages},
volume = {8},
number = {OOPSLA1},
year = {2024},
doi = {10.1145/3649814}
}