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• W2100060170 abstract "In recent years many methods and tools for software clone detection have been proposed. While some work has been done on assessing and comparing performance of these tools, very little empirical evaluation has been done. In particular, accuracy measures such as precision and recall have only been roughly estimated, due both to problems in creating a validated clone benchmark against which tools can be compared, and to the manual effort required to hand check large numbers of candidate clones. In this paper we propose an automated method for empirically evaluating clone detection tools that leverages mutation-based techniques to overcome these limitations by automatically synthesizing large numbers of known clones based on an editing theory of clone creation. Our framework is effective in measuring recall and precision of clone detection tools for various types of fine-grained clones in real systems without manual intervention." @default.
• W2100060170 created "2016-06-24" @default.
• W2100060170 creator A5008181644 @default.
• W2100060170 creator A5064499489 @default.
• W2100060170 date "2009-01-01" @default.
• W2100060170 modified "2023-10-09" @default.
• W2100060170 title "A Mutation/Injection-Based Automatic Framework for Evaluating Code Clone Detection Tools" @default.
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• W2100060170 doi "https://doi.org/10.1109/icstw.2009.18" @default.
• W2100060170 hasPublicationYear "2009" @default.
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