一种基于高阶函数摘要的依赖簇检测方法

杨嘉毅; 张迎周; 李俊锋; 马锐; 汪全盛; 薛渝川

doi:10.12263/DZXB.20230862

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一种基于高阶函数摘要的依赖簇检测方法

学术论文 | 更新时间：2025-12-08

- 一种基于高阶函数摘要的依赖簇检测方法
- A Dependence Clusters Detection Method Based on Higher-Order Function Summary
- 电子学报 2024年52卷第4期页码：1337-1348
- 作者机构：
  
  南京邮电大学计算机学院、软件学院、网络空间安全学院，江苏南京 210023
- 作者简介：
  
  [ "杨嘉毅男，1997年1月出生于江苏省徐州市.南京邮电大学硕士毕业生.主要研究领域为程序分析、函数式编程. E-mail: 18013977230@163.com" ]
  [ "张迎周男，1978年1月出生于安徽庐江.南京邮电大学教授.主要研究方向为软件与信息安全. E-mail: zhangyz@njupt.edu.cn" ]
  [ "李俊锋男，1999年5月出生于新疆维吾尔自治区奎屯市.南京邮电大学硕士生.主要研究方向为程序分析. E-mail: 1617736692@qq.com" ]
  [ "马锐男，2000年7月出生于江苏淮安.南京邮电大学硕士生.主要研究方向为程序分析. E-mail: 1607414781@qq.com" ]
  [ "汪全盛男，1999年12月出生于江苏省南京市.南京邮电大学硕士生.主要研究领域为程序分析.E-mail: 1366257509@qq.com" ]
  [ "薛渝川男，1999年1月出生于江苏省徐州市睢宁县.南京邮电大学硕士生.主要研究方向为程序分析. E-mail: 1842688656@qq.com" ]
- 基金信息：
  
  国家自然科学基金(62272214)
- DOI：10.12263/DZXB.20230862
  中图分类号： TP311
- 收稿：2023-09-13，
  
  修回：2023-12-08，
  
  纸质出版：2024-04-25
- 稿件说明：
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杨嘉毅,张迎周,李俊锋,等.一种基于高阶函数摘要的依赖簇检测方法[J].电子学报,2024,52(04):1337-1348.

YANG Jia-yi, ZHANG Ying-zhou, LI Jun-feng, et al.A Dependence Clusters Detection Method Based on Higher-Order Function Summary[J].Acta Electronica Sinica, 2024, 52(04): 1337-1348.
杨嘉毅,张迎周,李俊锋,等.一种基于高阶函数摘要的依赖簇检测方法[J].电子学报,2024,52(04):1337-1348. DOI：10.12263/DZXB.20230862

YANG Jia-yi, ZHANG Ying-zhou, LI Jun-feng, et al.A Dependence Clusters Detection Method Based on Higher-Order Function Summary[J].Acta Electronica Sinica, 2024, 52(04): 1337-1348. DOI：10.12263/DZXB.20230862

摘要

依赖簇是相互依赖的程序组件的最大集合，依赖簇中任意一点产生变动都会引起其他组件的连锁反应.在实际生产环境中，依赖簇检测对于软件理解、测试、维护具有非常重要的意义.传统的依赖簇检测方法基于系统依赖图（System Dependence Graph，SDG）实现过程间依赖关系的计算.但是SDG的构建过程比较复杂，时间空间的占用比较大.为提高依赖簇检测的效率并减少空间占用，本文提出了一种有效的轻量级依赖簇检测方法.该方法通过构建每个过程高阶函数形式的函数摘要，将形参和全局变量的数据依赖作为摘要参数，并用函数摘要的参数初始化过程内依赖信息.通过在调用点处对高阶函数形式的摘要进行实例化，即可将调用过程的依赖关系通过摘要参数进行传递，从而获取过程间的依赖信息.为了进一步提升检测效率，我们还提出了基于自适应计算的依赖簇检测优化策略，该策略可以减少因函数的递归调用产生的相关冗余计算.本文选取了不同规模不同领域的工程项目和基准测试集进行相关对比实验，结果表明：基于高阶函数的依赖簇检测方法相比系统依赖图的检测方法，能够提升2.689倍的分析效率并减少35.7%的空间占用；基于自适应计算的依赖簇检测优化策略在高阶函数方法的基础上能够减少56.7%的冗余计算，提升23.9%的分析效率.

Abstract

Dependence clusters are the largest sets of interdependent program components

where any change in one point of the cluster can trigger a chain reaction in other components. In practical production environments

detecting dependence clusters is of great importance for software understanding

testing

and maintenance. Traditional dependence cluster detection methods rely on the system dependence graph (SDG) to calculate dependencies

However

constructing an SDG is a complex process that incurs significant time and space costs when analyzing large programs. In order to improve the efficiency of cluster detection

this paper proposes a light-weight and efficient dependence cluster detection method based on higher-order functions

which can avoid constructing SDG in the analysis. This method constructs a function summary in the form of a higher-order function for each procedure

where the data dependencies of formal parameters and global variables are used to initialize the dependence inside the procedure. The dependence cluster information between procedures can be obtained by instantiating the function summary and passing the dependence through summary parameters at the call site

which avoids the construction of an SDG. To further improve the analysis efficiency of the higher-order function-based detection method

we propose an optimization strategy based on adaptive computing

which significantly reduces the redundant calculations caused by the mutual recursive calls between functions. In the end

we select benchmark test sets with different scales and fields and conduct relevant experiments

which demonstrate that the proposed program dependence cluster detection method based on higher-order functions can improve analysis efficiency by 268.9% and reduce space usage by 35.7% compared with the detection method based on SDG. The optimization strategy based on adaptive computing can reduce redundant calculations by 56.7% and improve analysis efficiency by 23.9% compared with the method based on higher-order functions.

关键词

Keywords

references

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