CP-Miner: finding copy-paste and related bugs in large-scale software code

By Zhenmin Li, Shan Lu, Suvda Myagmar, Yuanyuan Zhou. IEEE Transactions on Software Engineering, 2006

687 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Information Systems, Software

View Paper: https://www.semanticscholar.org/paper/82de0c1dadc711763ed7b4aa49e27c72522e6a49

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Citations in the corpus

VUDDY: A Scalable Approach for Vulnerable Code Clone Discovery (2017, background): “Due to CloSpan’s heuristics for improved efficiency, CP-Miner can scale to the size of moderately large code bases such as the Linux kernel, by consuming less memory space.”
Achieving accuracy and scalability simultaneously in detecting application clones on Android markets (2014, background): “Token-based approaches [40, 28, 29, 22] parse a program to a sequence of tokens and compare these tokens to find clones.”
A Large-Scale Study on Repetitiveness, Containment, and Composability of Routines in Open-Source Projects (2016, neutral): “The typical categories are text-based [16, 38], token-based [9, 27, 34, 42], tree-based [11, 25, 18], and graph-based [31, 36].”
Juxtapp: A Scalable System for Detecting Code Reuse among Android Applications (2012, background): “A variety of approaches for static code clone detection have been proposed in the programming language literature for refactoring, finding bugs, and better understanding of the code [21, 27, 29, 25].”
The road ahead for Mining Software Repositories (2008, background): “Other tools such as CP-Miner [40] can flag bugs by recognizing deviations in mined patterns for renaming variables when clonning (i.e., copy-and-paste) code.”
Shaping program repair space with existing patches and similar code (2018, methodology): “Consistent modifications (changes) [17, 32] are those modifications that require the same changes to a variable at different locations at the same time.”
Mining idioms from source code (2014, methodology): “ee fragments. The only work that we are aware of that uses language models for detecting textual patterns in code is Jacob and Tairas [21] that use n-grams to autocomplete code templates. Code clones [10, 25, 26, 27, 31, 43, 44] are related to code idiom mining, since they aim to nd highly similar c”
Using web corpus statistics for program analysis (2014, methodology): “In this section we analyze the effectiveness of our corpusdriven filters in improving the accuracy of plagiarism reports from MOSS [25], a widely-used modern plagiarism detector that can process JavaScript programs.”
NICAD: Accurate Detection of Near-Miss Intentional Clones Using Flexible Pretty-Printing and Code Normalization (2008, background): “Most lexical approaches [20, 4, 3, 25] (also called tokenbased approaches) are related to our work in the sense that like them we also use a similar sequence matching algorithm, and like them, we can also apply token transformation on the input.”
Software clone detection: A systematic review (2013, background): “Furthermore, it is easier to reuse the existing code than to develop a fresh solution since new code may introduce new errors [99,152].”
Asm2Vec: Boosting Static Representation Robustness for Binary Clone Search against Code Obfuscation and Compiler Optimization (2019, methodology): “CCFINDERX [42] and CP-Miner [43] use lexical tokens as features to find code clones.”
Comparison and evaluation of code clone detection techniques and tools: A qualitative approach (2009, methodology): “b The tool seems to have external dependencies or to be a part of a larger tool set T [86] (PROCSSI), T [92] (recoder), L [29] (PMD), L [84] (CloSpan), L [37] (ConQAT), S [15] (DMS [13]), S [14] (Bauhaus), [20] (CPython, ANTLR), S [111] (Microsoft Phoenix Framework), LS [74] (CodeDOM of .”
Deep learning code fragments for code clone detection (2016, background): “In this paper, our newfangled approach to mining internal source code representations gives way to a new, learning-based paradigm. classroom use is granted without fee provided that copies are not made or distributed for proﬁt or commercial advantage and that copies bear this notice and the full…”
To What Extent do Deep Learning-based Code Recommenders Generate Predictions by Cloning Code from the Training Set? (2022, background): “[40] showed that copy-pasting code can reduce the programming effort.”
Do code clones matter? (2009, background): “[26] present an approach to detect bugs based on inconsistent renaming of identifiers between clones.”
An Empirical Study of Function Clones in Open Source Software (2008, background): “For example, if a bug is detected in a code fragment, all the fragments similar to it should be investigated to check for the same bug [19], and when enhancing or adapting a piece of code, duplicated fragments can multiply the work to be done [12].”
A Survey on Software Clone Detection Research (2007, methodology): “Another state of the art token-based clone detection technique is CP-Miner [168, 169] where a frequent subsequence mining technique [3] is used for identifying a similar sequence of tokenized statements.”
Learning-Based Recursive Aggregation of Abstract Syntax Trees for Code Clone Detection (2019, methodology): “Besides clone detection, the notion of code similarity can be used in other applications like bug detection [2], [6], [7], performance prediction of code fragments [8], and information retrieval in software contexts [9].”
Learning to Fuzz: Application-Independent Fuzz Testing with Probabilistic, Generative Models of Input Data (2016, background): “, to find anomalies that correspond to bugs [36], for code completion [12], to recommend API usages [50], for plagiarism detection [31, 46], and to find copy-paste bugs [29].”