增量数据流分析中的常见线程:综合调查

IF 28 1区 计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS
Anushri Jana, Uday Khedker
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引用次数: 0

摘要

增量数据流分析采用仅基于代码修改部分更新数据流信息的技术,从而重用大量先前计算的信息。由于大多数现实世界的软件系统随着时间的推移而发展,增量分析技术提供了一种高效的,并且通常是从头开始的完整(重新)分析的唯一可行的替代方案。我们描述了现有的增量分析技术是如何归入一个通用的重置和重新计算范式的。这有双重好处。首先,它有助于我们根据增量技术如何适应这种范式来调查范围广泛的增量技术。其次,它使我们能够识别增量数据流分析领域的差距和开放挑战,以指导该领域的未来研究。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Common Threads in Incremental Data Flow Analysis: A Comprehensive Survey
Incremental data flow analysis employs techniques that update the data flow information based only on the modified parts of the code, thus reusing a lot of previously computed information. Since most real-world software systems evolve with time, incremental analysis techniques provide an efficient, and often the only feasible alternative to a complete (re)analysis from scratch. We describe how the existing incremental analysis techniques fall under a common reset and recompute paradigm. This has two-fold benefits. First, it facilitates us to survey a wide range of incremental techniques based on how they adapt this paradigm. Secondly, it enables us to identify gaps and open challenges in the field of incremental data flow analysis, to guide future research in this area.
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来源期刊
ACM Computing Surveys
ACM Computing Surveys 工程技术-计算机:理论方法
CiteScore
33.20
自引率
0.60%
发文量
372
审稿时长
12 months
期刊介绍: ACM Computing Surveys is an academic journal that focuses on publishing surveys and tutorials on various areas of computing research and practice. The journal aims to provide comprehensive and easily understandable articles that guide readers through the literature and help them understand topics outside their specialties. In terms of impact, CSUR has a high reputation with a 2022 Impact Factor of 16.6. It is ranked 3rd out of 111 journals in the field of Computer Science Theory & Methods. ACM Computing Surveys is indexed and abstracted in various services, including AI2 Semantic Scholar, Baidu, Clarivate/ISI: JCR, CNKI, DeepDyve, DTU, EBSCO: EDS/HOST, and IET Inspec, among others.
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