Similarity-based analyses on software applications: A systematic literature review

被引：15
作者：
Auch, Maximilian [1 ]
Weber, Manuel [1 ]
Mandl, Peter [1 ]
Wolff, Christian [2 ]
机构：
[1] Munich Univ Appl Sci, Lothstr 64, D-80335 Munich, Germany
[2] Univ Regensburg, Regensburg, Germany
来源：
JOURNAL OF SYSTEMS AND SOFTWARE | 2020年 / 168卷
关键词：
Software similarity; Secondary study; Machine learning; AUTOMATIC CATEGORIZATION; PROJECTS; CLASSIFICATION; BYTECODE;
D O I：
10.1016/j.jss.2020.110669
中图分类号：
TP31 [计算机软件];
学科分类号：
081202 ; 0835 ;
摘要：
In empirical studies on processes, practices, and techniques of software engineering, automation and machine learning are gaining popularity. In order to extract knowledge from existing software projects, a sort of similarity analysis is often performed using different methodologies, data and metadata. This systematic literature review focuses therefore on existing approaches of similarity-, categorization-and relevance-based analysis on software applications. In total, 136 relevant publications and patents were identified between 2002 and 2019 according to the established inclusion and exclusion criteria, which perform a calculation of software similarity in general or to support certain software engineering phases. (C) 2020 Elsevier Inc. All rights reserved.
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页数：25
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