Detecting higher-level similarity patterns in programs

Basit, Hamid Abdul; Jarzabek, Stan

doi:10.1145/1081706.1081733

Cited by 51 publications

(29 citation statements)

References 34 publications

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“…Similar to plagiarism detection, clone detection identifies cloned fragments by abstracting the program into a set of characteristics. The abstraction can be categorized into the following types: String-based [58], [59], Token-based [54], [55], AST-based [57], [60]- [62], PDG-based [63]- [65], Behavior-based [66], and Memory-State-based [56]. Sebjornsen et.…”

Section: Related Work In Other Domainsmentioning

confidence: 99%

Software Plagiarism Detection with Birthmarks Based on Dynamic Key Instruction Sequences

Tian

Zheng

Liu

et al. 2015

IIEEE Trans. Software Eng.

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A software birthmark is a unique characteristic of a program. Thus, comparing the birthmarks between the plaintiff and defendant programs provides an effective approach for software plagiarism detection. However, software birthmark generation faces two main challenges: the absence of source code and various code obfuscation techniques that attempt to hide the characteristics of a program. In this paper, we propose a new type of software birthmark called DYKIS (DYnamic Key Instruction Sequence) that can be extracted from an executable without the need for source code. The plagiarism detection algorithm based on our new birthmarks is resilient to both weak obfuscation techniques such as compiler optimizations and strong obfuscation techniques implemented in tools such as SandMark, Allatori and Upx. We have developed a tool called DYKIS-PD (DYKIS Plagiarism Detection tool) and conducted extensive experiments on large number of binary programs. The tool, the benchmarks and the experimental results are all publicly available.Index Terms-software plagiarism detection, software birthmark ! 0098-5589 (c)

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Section: Related Work In Other Domainsmentioning

confidence: 99%

Software Plagiarism Detection with Birthmarks Based on Dynamic Key Instruction Sequences

Tian

Zheng

Liu

et al. 2015

IIEEE Trans. Software Eng.

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“…Basit and Stan apply the Frequent Itemset data mining techniques in order to detect design level clones using the clone information [18]. Cory and Godfrey propose to understand clone in large software systems through categorization [28].…”

Section: Related Workmentioning

confidence: 99%

A Framework for Studying Clones In Large Software Systems

Jiang¹,

Hassan²

2007

Seventh IEEE International Working Conference on Source Code Analysis and Manipulation (SCAM 2007)

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Clones are code segments that have been created by copying-and-pasting from other code segments. Clones occur often in large software systems. It is reported that 5 to 50% of the source code of a large software system is cloned. A major challenge when studying code cloning in large software systems is handling the large amount of clone candidates produced by leading edge clone detection tools. For example, the CCFinder, clone detection tool, produces over 7 million pairs of clone candidates for the Linux kernel (which consists of over 4 MLOC). Moreover, the output of clone detection tools grows rapidly as a software system evolves. Researchers and developers need tools to help them study the large amount of clone data in order to better understand the clone phenomena in large systems. In this paper, we propose a data mining framework to help researchers cope with the large amount of data produced by clone detection tools. We propose techniques to reduce, abstract and highlight the most interesting data produced by clone detection tools. Our framework also introduces a visualization tool which allows users to query and explore clone data at various abstraction levels. We demonstrate our framework on a case study of the clone phenomena in the Linux kernel.

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“…It is also the first attempt to address filtering and visualization of higher-level cloning abstractions formed by Clone Miner that represent larger, designlevel software similarities [2].…”

Section: Introductionmentioning

confidence: 99%

“…We developed our approach to enable the follow up analysis of cloning data produced by our clone detector called Clone Miner [2]. Unlike most of the other clone detectors, Clone Miner not only finds simple clones, but also presents further abstractions of these clones in the form of large granularity similar program structures such as similar files or repeating configurations of similar files across directories.…”

Section: Introductionmentioning

confidence: 99%

“…Unlike most of the other clone detectors, Clone Miner not only finds simple clones, but also presents further abstractions of these clones in the form of large granularity similar program structures such as similar files or repeating configurations of similar files across directories. Our previous research showed that elevating software similarity analysis to such large granularity similar program structures improves our understanding of the overall similarity situation in a subject system [2]. Clone Miner uses data mining techniques to detect these cloning abstractions as recurring configurations of simple clones or other lower-level cloning abstractions [2].…”

Section: Introductionmentioning

confidence: 99%

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Query-based filtering and graphical view generation for clone analysis

Zhang

Basit

Jarzabek

et al. 2008

2008 IEEE International Conference on Software Maintenance

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Code clones are similar program structures recurring in software systems. Clone detectors produce much information and a challenge is to identify useful clones depending on the goals of clone analysis. To do so, further abstraction, filtering and visualization of cloning information, with the involvement of a human expert, is required. In this paper, we describe a technique for filtering and visualization of cloning information generated by Clone Miner, a clone detection tool presented in our earlier work. Unique benefit and contribution of our approach is that a human expert can define a wide range of filters to extract abstract views of the cloning data using a clone-query system to suit specific needs of clone analysis. We then produce standardized graphical presentations of those views for various types of clone queries. We implemented the technique into an Eclipse plug-in called Clone Visualizer. Clone Visualizer works closely with Clone Miner which not only finds similar code fragments (simple clones) but also finds higher-level abstractions of the cloning information. Our method is the first attempt to address filtering and visualization of those higher level cloning abstractions. We illustrate application of our technique with examples from a clone analysis project with Clone Miner and Clone Visualizer.978-1-4244-2614-0/08/$25.00

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Detecting higher-level similarity patterns in programs

Cited by 51 publications

References 34 publications

Software Plagiarism Detection with Birthmarks Based on Dynamic Key Instruction Sequences

Software Plagiarism Detection with Birthmarks Based on Dynamic Key Instruction Sequences

A Framework for Studying Clones In Large Software Systems

Query-based filtering and graphical view generation for clone analysis

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