Comparison and evaluation of code clone detection techniques and tools: A qualitative approach

By Chanchal Kumar Roy, James R. Cordy, Rainer Koschke. Science of Computer Programming, 2009

1,141 citations recorded by the source corpus.

Subfields: Information Systems, Signal Processing

View Paper: https://www.semanticscholar.org/paper/fb35f65339c4f1bbe9c0bd25f09f83e65e436d51

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Citations in the corpus

A Large-Scale Study on Repetitiveness, Containment, and Composability of Routines in Open-Source Projects (2016, background): “The practice of copying-and-pasting source code leads to cloned code [12, 57].”
Deep learning code fragments for code clone detection (2016, background): “Tractability is realized by decomposing a joint distribution and analyzing probabilistic automata, e.g., n -grams: p ( s ) = =1 p ( w i | w i − 1”
Mining idioms from source code (2014, background): “Code clones [6, 11, 26, 27, 28, 32, 44, 45] are related to idiom mining, since they aim to find blocks of highly similar code.”
Towards a Big Data Curated Benchmark of Inter-project Code Clones (2014, neutral): “Researchers agree upon four primary clone types [1], [8].”
Detection (2020, neutral): “Since all 16 cases presented in [12] have small Levenshtein distance, the technique can easily f nd all clones in these categories and thus should perform better than the current state-of-the-art techniques.”
MISIM: An End-to-End Neural Code Similarity System (2020, neutral): “a type 4 code similarity exercise [36]).”
Cross-project code clones in GitHub (2018, background): “In general, two non-overlapping code fragments C1 and C2 are clones, if C1 and C2 are “similar by some given definition of similarity” (Roy et al. 2009).”
A measurement study of google play (2014, background): “PlayDrone ’s better performance can be explained by classifying code cloning techniques [32].”
Preventing Verbatim Memorization in Language Models Gives a False Sense of Privacy (2022, background): “Existing plagiarism tools, and their evaluations, go far beyond verbatim matches and consider fuzzy data “clones” ranging from simple transformations (e.g., word variations or shuffles) to arbitrary semantics-preserving para-phrasing (Roy et al., 2009; Potthast et al., 2010).”
DeepSim: deep learning code functional similarity (2018, background): “Measuring similarity between code fragments is fundamental for many software engineering tasks, e.g., code clone detection [44], code search and reuse [23, 30], refactoring [20, 61], bug detection [27, 36].”
VUDDY: A Scalable Approach for Vulnerable Code Clone Discovery (2017, methodology): “To avoid confusion concerning the various taxonomies adopted in other research, we use the following well accepted ([20], [21], [22], [23]) definitions of the types of code clones.”
SourcererCC: Scaling Code Clone Detection to Big-Code (2015, methodology): “Further details, including a list of the mutation operators, is available in our earlier studies [23, 27, 35].”
Building Program Vector Representations for Deep Learning (2014, methodology): “…metrics can also be used to measure similarity—like [50] in NLP and [18], [24] in program analysis, which may be useful for code clone detection [51], code retrieval [25]—they fail to capture different aspects of the relationships between different symbols because the similarity is the only…”
Detecting Similar Programs via The Weisfeiler-Leman Graph Kernel (2016, background): “Previous research in program similarity has focused more on detecting syntactic similarity [24] and less on detecting semantic similarity, as the latter is generally undecidable.”