Progress in Parallelization of Static Program Analysis

被引：0
作者：
Lu S.-M. [1 ]
Zuo Z.-Q. [1 ]
Wang L.-Z. [1 ]
机构：
[1] State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing
来源：
Ruan Jian Xue Bao/Journal of Software | 2020年 / 31卷 / 05期
基金：
中国国家自然科学基金;
关键词：
Distributed processing; GPU acceleration; Parallel computing; Software quality assurance; Static program analysis;
D O I：
10.13328/j.cnki.jos.005950
中图分类号：
学科分类号：
摘要：
After years of research, static program analysis has made great progress in many aspects. However, performing sophisticated program analysis over large-scale software systems (such as Linux kernel, Hadoop, etc. ) is still challenging due to its high complexity. To address the poor scalability of static analysis, with the rise of multi-core computation architectures, researchers have proposed various parallel static analysis techniques. This paper first introduces the basic concepts of static analysis, the key techniques, and the challenges. Then the traditional optimization approaches are discussed followed by the studies in parallelizing static analysis in three categories-CPU parallelism, distributed and GPU implementation, and the representative parallel static analysis tools. Finally, the potential research trend in parallelizing static analysis is described. © Copyright 2020, Institute of Software, the Chinese Academy of Sciences. All rights reserved.
引用
收藏
页码：1243 / 1254
页数：11
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