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Titlebook: Code Clone Analysis; Research, Tools, and Katsuro Inoue,Chanchal K. Roy Book 2021 The Editor(s) (if applicable) and The Author(s), under ex

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發(fā)表于 2025-3-21 18:40:12 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Code Clone Analysis
副標題Research, Tools, and
編輯Katsuro Inoue,Chanchal K. Roy
視頻videohttp://file.papertrans.cn/229/228809/228809.mp4
概述Is the first book on code clone analysis.Covers topics from foundations to practices.Collects important research publications updated with new results and future directions
圖書封面Titlebook: Code Clone Analysis; Research, Tools, and Katsuro Inoue,Chanchal K. Roy Book 2021 The Editor(s) (if applicable) and The Author(s), under ex
描述.This is the first book organized around code clone analysis. To cover the broad studies of code clone analysis, this book selects past research results that are important to the progress of the field and updates them with new results and future directions.?..The first chapter provides an introduction for readers who are inexperienced in the foundation of code clone analysis, defines clones and related terms, and discusses the classification of clones. The chapters that follow are categorized into three main parts to present 1) major tools for code clone analysis, 2) fundamental topics such as evaluation benchmarks, clone visualization, code clone searches, and code similarities, and 3) applications to actual problems. Each chapter includes a valuable reference list that will help readers to achieve a comprehensive understanding of this diverse field and to catch up with the latest research results.?..Code clone analysis relies heavily on computer science theories such as pattern matching algorithms, computer language, and software metrics. Consequently, code clone analysis can be applied to a variety of real-world tasks in software development and maintenance such as bug finding a
出版日期Book 2021
關(guān)鍵詞Code Clone Analysis; Software Maintenance; Software Engineering; Program Reuse; Program Reuse; Copy and P
版次1
doihttps://doi.org/10.1007/978-981-16-1927-4
isbn_softcover978-981-16-1929-8
isbn_ebook978-981-16-1927-4
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor
The information of publication is updating

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CCLearner: Clone Detection via Deep Learning, NiCad, and Deckard. CCL. outperformed existing tools by achieving a better trade-off between precision and recall. To further investigate whether other machine learning algorithms can perform comparatively as deep learning, we replaced deep learning with five alternative machine learning algorithm
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BigCloneBenchse-case. We have used BigCloneBench in a number of studies that demonstrate its value, as well as show where it has been used by the research community. In this chapter, we discuss the clone benchmarking theory and the existing benchmarks, describe the BigCloneBench creation process, and overview th
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Source Code Clone Searchof the internals of such an engine. Finally, we discuss how to benchmark and evaluate the performance of clone search engines. The discussion includes a set of measures that are helpful in evaluating clone search engines.
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Is Late Propagation a Harmful Code Clone Evolutionary Pattern? An?Empirical Studyproneness. Our results confirm that late propagation is the more harmful clone evolution pattern and that some specific cases of late propagations are more harmful than others. We trained machine learning models using 18 clone evolution related features to predict the evolution of late propagation a
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