Using Slicing to Identify Duplication in Source Code

Komondoor, Raghavan; Horwitz, Susan

doi:10.1007/3-540-47764-0_3

Cited by 339 publications

(256 citation statements)

References 21 publications

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“…Higo et al (2011) extended this to an incremental approach. Komondoor & Horwitz (2001) also used PDGs for clone detection and see the possibility to find non-contiguous clones as a main benefit. Gabel, Jiang & Su (2008) combined the analysis of dependence graphs with abstract syntax trees in the tool Deckard to better scale the approach.…”

Section: Related Workmentioning

confidence: 99%

How are functionally similar code clones syntactically different? An empirical study and a benchmark

Wagner

Abdulkhaleq

Bogicevic

et al. 2016

PeerJ Computer Science

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Background. Today, redundancy in source code, so-called ''clones'' caused by copy &paste can be found reliably using clone detection tools. Redundancy can arise also independently, however, not caused by copy&paste. At present, it is not clear how only functionally similar clones (FSC) differ from clones created by copy&paste. Our aim is to understand and categorise the syntactical differences in FSCs that distinguish them from copy&paste clones in a way that helps clone detection research. Methods. We conducted an experiment using known functionally similar programs in Java and C from coding contests. We analysed syntactic similarity with traditional detection tools and explored whether concolic clone detection can go beyond syntax. We ran all tools on 2,800 programs and manually categorised the differences in a random sample of 70 program pairs. Results. We found no FSCs where complete files were syntactically similar. We could detect a syntactic similarity in a part of the files in <16% of the program pairs. Concolic detection found 1 of the FSCs. The differences between program pairs were in the categories algorithm, data structure, OO design, I/O and libraries. We selected 58 pairs for an openly accessible benchmark representing these categories. Discussion. The majority of differences between functionally similar clones are beyond the capabilities of current clone detection approaches. Yet, our benchmark can help to drive further clone detection research.

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Section: Related Workmentioning

confidence: 99%

How are functionally similar code clones syntactically different? An empirical study and a benchmark

Wagner

Abdulkhaleq

Bogicevic

et al. 2016

PeerJ Computer Science

View full text Add to dashboard Cite

show abstract

“…Many approaches have been developed over the years to detect code clones [20,23,25,26]. A code clone is two or more segments of code that have the same semantics but come from different sources.…”

Section: Code Clones and Reuse Detectionmentioning

confidence: 99%

AnDarwin: Scalable Detection of Semantically Similar Android Applications

Crussell

Gibler

Chen

2013

Lecture Notes in Computer Science

112

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Abstract. The popularity and utility of smartphones rely on their vibrant application markets; however, plagiarism threatens the long-term health of these markets. We present a scalable approach to detecting similar Android apps based on their semantic information. We implement our approach in a tool called AnDarwin and evaluate it on 265,359 apps collected from 17 markets including Google Play and numerous thirdparty markets. In contrast to earlier approaches, AnDarwin has four advantages: it avoids comparing apps pairwise, thus greatly improving its scalability; it analyzes only the app code and does not rely on other information -such as the app's market, signature, or descriptionthus greatly increasing its reliability; it can detect both full and partial app similarity; and it can automatically detect library code and remove it from the similarity analysis. We present two use cases for AnDarwin: finding similar apps by different developers ("clones") and similar apps from the same developer ("rebranded"). In ten hours, AnDarwin detected at least 4,295 apps that have been the victims of cloning and 36,106 apps that are rebranded. By analyzing the clusters found by AnDarwin, we found 88 new variants of malware and identified 169 malicious apps based on differences in the requested permissions. Our evaluation demonstrates AnDarwin's ability to accurately detect similar apps on a large scale.

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“…Previous works include a wide range of techniques from code compression [10][11][12][13] to procedure abstraction [14][15][16] and dead code elimination [17].…”

Section: Related Workmentioning

confidence: 99%

“…Komondoor and Horvitz propose procedure extraction at the source code level [14,15]. Zmily and Kozyrakis propose BLISS which successfully targets reducing text space, energy use and execution time [20].…”

Section: Related Workmentioning

confidence: 99%

Profile-Driven Selective Program Loading

Ince

Hollingsworth

2010

Euro-Par 2010 - Parallel Processing

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Abstract. Complex software systems use many shared libraries frequently composed of large off-the-shelf components. Only a limited number of functions are used from these shared libraries. Historically demand paging prevented this from wasting large amounts of memory. Many high end systems lack virtual memory and thus must load the entire shared library into each node's memory. In this paper we propose a system which decreases the memory footprint of applications by selectively loading only the used portions of the shared libraries. After profiling executables and shared libraries, our system rewrites all target shared libraries with a new function ordering and updated ELF program headers so that the loader only loads those functions that are likely to be used by a given application and includes a fallback user-level paging system to recover in the case of failures in our analysis. We present a case study that shows our system achieves more than 80% reduction in the number of pages that are loaded for several HPC applications while causing no performance overhead for reasonably long running programs.

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Using Slicing to Identify Duplication in Source Code

Cited by 339 publications

References 21 publications

How are functionally similar code clones syntactically different? An empirical study and a benchmark

How are functionally similar code clones syntactically different? An empirical study and a benchmark

AnDarwin: Scalable Detection of Semantically Similar Android Applications

Profile-Driven Selective Program Loading

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