Index-based code clone detection: incremental, distributed, scalable

Hummel, Benjamin; Juergens, Elmar; Heinemann, Lars; Conradt, Michael

doi:10.1109/icsm.2010.5609665

Cited by 109 publications

(100 citation statements)

References 29 publications

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“…Hummel et al [14] were the first to use an index-based approach to scale clone detection to large inter-project repositories. Their technique detects only the Type-1 and Type-2 clones.…”

Section: Related Workmentioning

confidence: 99%

“…General purpose clone detection is required for clone studies in large inter-project repositories and to help developers manage and merge their related software forks, as well as for use in the domain-specific activities. Scalable general purpose clone detection has been achieved by using deterministic [21] or non-deterministic [31] input partitioning and distributed execution of an existing non-scalable detector, using large distributed code indexes [14], or by comparing hashes after Type-1/2 normalization [15]. These existing techniques have a number of limitations.…”

Section: Introductionmentioning

confidence: 99%

“…These existing techniques have a number of limitations. The novel scalable algorithms [14,15] do not support Type-3 near-miss clones, where minor to significant editing activities might have taken place in the copy/pasted fragments, and therefore miss a large portion of the clones, since there are more Type-3 in the repositories than other types [24,26,30]. Type-3 clones can be the most needed in large-scale clone detection applications [24,19,6].…”

Section: Introductionmentioning

confidence: 99%

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SourcererCC

Sajnani

Saini

Svajlenko

et al. 2016

Proceedings of the 38th International Conference on Software Engineering

315

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Despite a decade of active research, there is a marked lack in clone detectors that scale to very large repositories of source code, in particular for detecting near-miss clones where significant editing activities may take place in the cloned code. We present SourcererCC, a token-based clone detector that targets three clone types, and exploits an index to achieve scalability to large inter-project repositories using a standard workstation. SourcererCC uses an optimized invertedindex to quickly query the potential clones of a given code block. Filtering heuristics based on token ordering are used to significantly reduce the size of the index, the number of code-block comparisons needed to detect the clones, as well as the number of required token-comparisons needed to judge a potential clone.We evaluate the scalability, execution time, recall and precision of SourcererCC, and compare it to four publicly available and state-of-the-art tools. To measure recall, we use two recent benchmarks, (1) a large benchmark of real clones, BigCloneBench, and (2) a Mutation/Injection-based framework of thousands of fine-grained artificial clones. We find SourcererCC has both high recall and precision, and is able to scale to a large inter-project repository (250MLOC) using a standard workstation.

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“…Hummel et al [14] were the first to use an index-based approach to scale clone detection to large inter-project repositories. Their technique detects only the Type-1 and Type-2 clones.…”

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

SourcererCC

Sajnani

Saini

Svajlenko

et al. 2016

Proceedings of the 38th International Conference on Software Engineering

315

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show abstract

“…Hummel et al proposed indexbased code clone detection approach [19]. It detects Type-1 and Type-2 clones using MD5 hash function to calculate hash values from normalized statements.…”

Section: Related Workmentioning

confidence: 99%

Proposing and Evaluating Clone Detection Approaches with Preprocessing Input Source Files

Choi

Yoshida

Higo

et al. 2015

IEICE Trans. Inf. & Syst.

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SUMMARYSo far, many approaches for detecting code clones have been proposed based on the different degrees of normalizations (e.g. removal of white spaces, tokenization, and regularization of identifiers). Different degrees of normalizations lead to different granularities of source code to be detect as code clones. To investigate how the normalizations impact the code clone detection, this study proposes six approaches for detecting code clones with preprocessing input source files using different degrees of normalizations. More precisely, each normalization is applied to the input source files and then equivalence class partitioning is performed to the files in the preprocessing. After that, code clones are detected from a set of files that are representatives of each equivalence class using a token-based code clone detection tool named CCFinder. The proposed approaches can be categorized into two types, approaches with non-normalization and normalization. The former is the detection of only identical files without any normalization. Meanwhile, the latter category is the detection of identical files with different degrees of normalizations such as removal of all lines containing macros. From the case study, we observed that our proposed approaches detect code clones faster than the approach that uses only CCFinder. We also found the approach with nonnormalization is the fastest among the proposed approaches in many cases.

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“…It is not easy to maintain the clone of the code as compared to the original code [2]. Hence there is a need to remove the clones from the code, since cloning leaves adverse effects on the maintenance phase of the software [3].…”

Section: Introductionmentioning

confidence: 99%

A Survey on Trending Algorithms for Software Code Clone Detection

Sidhu¹

2017

IJCA

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Index-based code clone detection: incremental, distributed, scalable

Cited by 109 publications

References 29 publications

SourcererCC

SourcererCC

Proposing and Evaluating Clone Detection Approaches with Preprocessing Input Source Files

A Survey on Trending Algorithms for Software Code Clone Detection

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