A Survey on Software Clone Detection Research

By Chanchal Kumar Roy, James R. Cordy. 2007

956 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Jian Zhang
2. Ge Li
3. Michele Tufano
4. Hongyu Zhang
5. Hao Yu
6. Chanchal Kumar Roy
7. Xu Wang
8. Miltiadis Allamanis

Citations in the corpus

• Mining idioms from source code (2014, methodology): “ as experienced Java programmers ourselves, we would nd this alien and more difcult to understand. Idioms dier signicantly from previous notions of textual patterns in software, such as code clones [43] and API patterns [55]. Unlike clones, idioms commonly recur across projects, even ones from diere”
• Comparison and evaluation of code clone detection techniques and tools: A qualitative approach (2009, background): “Deriving such scenarios is itself challenging, since the definition of clones is inherently vague in the literature [102, 73].”
• An Empirical Study of Function Clones in Open Source Software (2008, methodology): “These validated results can be used as a cloning reference for these systems and as a benchmark for evaluating other clone detection tools.”
• Detection (2020, unknown): “For a detailed survey of clone detection techniques, a good portrait is provided in [11].”
• SourcererCC: Scaling Code Clone Detection to Big-Code (2015, background): “ection we measure the recall of SourcererCC and the competing tools. Recall has been very dicult for tool developers to measure as it requires knowledge of the clones that exist in a software system [25, 24]. Manually inspecting a system for clones is non-trivial. Even a small system like Cook, when”
• Do code clones matter? (2009, methodology): “The literature provides a wide variety of different definitions of clones and clone related terms [20, 29].”
• A Novel Neural Source Code Representation Based on Abstract Syntax Tree (2019, background): “2) Code Clone Detection: There are generally four different types of code clones [54].”
• Software clone detection: A systematic review (2013, background): “Roy and Cordy [187] mentions code duplication or cloning as a form of software reuse.”
• Detecting Code Clones with Graph Neural Network and Flow-Augmented Abstract Syntax Tree (2020, background): “According to [1], code clones can be categorized into the following four types:”
• DeepSim: deep learning code functional similarity (2018, unknown): “According to Carter et al. [12], Roy and Cordy [42], code clones can be roughly classified into four types.”
• Supervised Deep Features for Software Functional Clone Detection by Exploiting Lexical and Syntactical Information in Source Code (2017, methodology): “Software clone can be categorized into four different types based on different levels of similarity [Roy and Cordy, 2007]: Type-1: identical code fragments in addition to variations in comments and layout; Type-2: apart from Type-1 clones, identical code fragments except for different identifier…”
• Deep learning code fragments for code clone detection (2016, background): “A statistical language model is a probability distribution over sentences in a language [57].”
• An Evaluation Framework for Plagiarism Detection (2010, background): “There are a few surveys about automatic plagiarism detection in text [7, 8, 14] and in code [12, 17, 19, 20].”
• Learning-Based Recursive Aggregation of Abstract Syntax Trees for Code Clone Detection (2019, background): “In 2007, three surveys [10]–[12] described, categorized and evaluated the existing approaches to code clone detection.”