算法工程方法论

IF 28 1区计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS

ACM Computing Surveys Pub Date : 2025-09-22 DOI:10.1145/3769071

Jan Mendling, Henrik Leopold, Henning Meyerhenke, Benoit Depaire

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引用次数: 0

摘要

近年来，对算法的研究急剧增加。计算机科学的各个分支学科根据不同的目标和标准研究算法。这一领域的多元性导致了各种尚未转移到邻近子学科的方法学进步。更好地进行知识交流的主要障碍是缺乏整合这些子学科观点的共同方法框架。本文的目标是为算法工程开发这样一个研究框架。我们的框架建立在科学哲学中讨论的三个领域：本体论、认识论和方法论。该框架帮助我们识别和讨论与计算机科学各个领域的算法贡献相关的各种有效性问题。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Methodology of Algorithm Engineering

Research on algorithms has drastically increased in recent years. Various sub-disciplines of computer science investigate algorithms according to different objectives and standards. This plurality of the field has led to various methodological advances that have not yet been transferred to neighboring sub-disciplines. The central roadblock for a better knowledge exchange is the lack of a common methodological framework integrating the perspectives of these sub-disciplines. It is the objective of this paper to develop such a research framework for algorithm engineering. Our framework builds on three areas discussed in the philosophy of science: ontology, epistemology and methodology. The framework helps us to identify and discuss various validity concerns relevant for any contribution on algorithms in various areas of computer science.

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来源期刊

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.