Identifying similar code with program dependence graphs

By Jens Krinke. Proceedings Eighth Working Conference on Reverse Engineering, 2001

740 citations recorded by the source corpus.

Subfields: Information Systems, Software, Signal Processing

View Paper: https://www.semanticscholar.org/paper/9bdac1f28f7d4c5f3b28c25ea5bbc6a3be0449b1

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Citations in the corpus

Automatically generating commit messages from diffs using neural machine translation (2017, background): “For example, to detect code clones, traditional approaches predeﬁne the representations of code fragments (some techniques use token sequences to represent code [25]; others use graphs [29], [9]).”
Predicting source code changes by mining change history (2004, background): “Many of these approaches defined clones based on various program aspects such as software metrics (e.g., [17]), the program’s text (e.g., [5]), abstract syntax trees (e.g., [6]), and program dependency graphs (e.g., [ 14 ]).”
CP-Miner: A Tool for Finding Copy-paste and Related Bugs in Operating System Code (2004, methodology): “The techniques used include: line-by-line [6], token-by-token [24, 33], (cid:2)ngerprinting [21], visualization [11, 13], abstract syntax tree [7, 27], and dependence graph [26, 28].”
Comparison and Evaluation of Clone Detection Tools (2007, neutral): “Ç”
Do code clones matter? (2009, background): “• Tree [3,8,12] and graph [9,19,22] based approaches require the availability of suitable context free grammars for AST or PDG construction.”
DeepSim: deep learning code functional similarity (2018, background): “There exist a few techniques [15, 17, 32, 37] that construct a program dependence graph (PDG) for each code fragment and measure code similarity through finding isomorphic sub-graphs.”
Finding Clones with Dup: Analysis of an Experiment (2007, background): “Duplix (based on program dependency graphs) reported only Type 3 clones [11].”
Survey of Research on Software Clones (2006, background): “identified as isomorphic subgraphs in a program dependency graph [51, 52].”
Graph-based mining of multiple object usage patterns (2009, methodology): “Some clone detection approaches applied graph-based techniques, but are limited in scalability [17].”
Clone Detection Using Abstract Syntax Suffix Trees (2006, result): “The paper reports the results of several large case studies in which we empirically compare the new technique to other techniques using the Bellon benchmark for clone detectors.”
To What Extent do Deep Learning-based Code Recommenders Generate Predictions by Cloning Code from the Training Set? (2022, methodology): “These techniques exploit information extracted from the AST [6, 8, 52], use a graph-based representation of code [23, 36, 37], or look at token-based similarities [28, 29, 33].”
A Large-Scale Study on Repetitiveness, Containment, and Composability of Routines in Open-Source Projects (2016, background): “Duplix [33] ﬁnds similar subgraphs in the PDGs to detect clones.”
Comparison and evaluation of code clone detection techniques and tools: A qualitative approach (2009, methodology): “In fact, in Bellon’s experiment [18], the graph-based tool Du-plix [75] was found to detect a small proportion of such near-miss clones.”
CP-Miner: finding copy-paste and related bugs in large-scale software code (2006, background): “…drivers, arch, and crypt have higher percentage of copy-paste than other modules in Linux; and f. as the operating system code evolves, the amount of copy-paste also increases, but the coverage percentage of copy-pasted code remains relatively stable over the recent versions of Linux and FreeBSD.”
GPLAG: detection of software plagiarism by program dependence graph analysis (2006, methodology): “Moreover, for both effectiveness and efficiency, a statistical lossy filter is developed, which has not been seen in previous studies [10, 12].”
The New C Standard (Excerpted material) An Economic and Cultural Commentary (2008, positive): “However, the dependencies between statements will not be affected by any differences in ordering and comparisons based on a dependence graph will be able to find duplicates.[ 23 ]”
A Survey on Software Clone Detection Research (2007, methodology): “Krinke uses an iterative approach (k-length patch matching) for detecting maximal similar subgraphs [156].”