NICAD: Accurate Detection of Near-Miss Intentional Clones Using Flexible Pretty-Printing and Code Normalization

By Chanchal Kumar Roy, James R. Cordy. IEEE International Conference on Program Comprehension, 2008

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An Empirical Study on Learning Bug-Fixing Patches in the Wild via Neural Machine Translation (2018, methodology): “In order to better check against the presence of clones, we also employ a state-of-the-art clone detection tool, NiCad [69].”
Do code clones matter? (2009, background): “Text Normalized code fragments are compared textually in a pairwise fashion [30].”
DeepSim: deep learning code functional similarity (2018, background): “Existing code clone detectors mainly target type I-III code clones through measuring code syntactical similarity based on representations such as text [6, 7, 43], tokens [28, 36, 47], abstract syntax tree [10] or parse-tree [26].”
Comparison and evaluation of code clone detection techniques and tools: A qualitative approach (2009, methodology): “…Basic NICAD [99] Syntactic pretty-printing with ﬂexible code normalization and ﬁltering, then textual comparison with thresholds Full NICAD [104] Transformation to a middle format of atomic instructions and edit distance algorithm Nasehi [92] Textual comparison with ﬂexible options (e.g.,…”
An Empirical Study of Function Clones in Open Source Software (2008, methodology): “In particular, we compute the size-sensitive Unique Percentage of Items (UPI) for each potential clone usng the equation (for details see [23]):”