Towards a Big Data Curated Benchmark of Inter-project Code Clones

Svajlenko, Jeffrey; Islam, Judith F.; Keivanloo, Iman; Roy, Chanchal K.; Mia, Mohammad Mamun

doi:10.1109/icsme.2014.77

Cited by 238 publications

(172 citation statements)

References 12 publications

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“…We then demonstrate SourcererCC's execution for a Big Data inter-project repository, one of the prime targets of scalable clone detection. We measure its clone recall using two benchmarks: The Mutation and Injection Framework [23,35] and BigCloneBench [30,34]. We measure the precision of our tool by manually validating a statistically significant sample of its output for the BigCloneBench experiment.…”

Section: Discussionmentioning

confidence: 99%

“…However, we recognized that a modern benchmark of real clones is also required. So we developed an efficient clone validation strategy based on code functionality and built BigCloneBench [30], a Big Data clone benchmark containing 8 million validated clones within and between 25,000 opensource projects. It measures recall for an extensive variety of real clones produced by real developers.…”

Section: Recallmentioning

confidence: 99%

“…BigCloneBench [30] is a Big Data clone benchmark of manually validated clone pairs in the inter-project software repository IJaDataset 2.0 [3]. IJaDataset consists of 25,000 open-source Java systems spanning 3 million files and 250MLOC.…”

Section: Recall Measured By Bigclonebenchmentioning

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%

“…We measure its clone recall using two proven [33,34] clone benchmarks. We use BigCloneBench [30], a Big Data benchmark of real clones that spans the four primary clone types and the full spectrum of syntactical similarity. We also use The Mutation and Injection Framework [35,23], a synthetic benchmark that can precisely measure recall at a fine granularity.…”

Section: Introductionmentioning

confidence: 99%

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SourcererCC

Sajnani

Saini

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et al. 2016

Proceedings of the 38th International Conference on Software Engineering

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354

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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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Section: Discussionmentioning

confidence: 99%

Section: Recallmentioning

confidence: 99%

Section: Recall Measured By Bigclonebenchmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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SourcererCC

Sajnani

Saini

Svajlenko

et al. 2016

Proceedings of the 38th International Conference on Software Engineering

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354

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Intercomponent Dependency Issues in Software Ecosystems

Claes

Decan

Mens

2018

Software Technology: 10 Years of Innovation in IEEE Computer

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Focus : Function clone identification on cross‐platform

Liu

et al. 2021

Int J of Intelligent Sys

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Automatic identification of function clones on crossplatform aims at determining whether two functions are identical or not without access to the source code, which is a fundamental challenge in vulnerability search, code plagiarism detection, and malware classification. With the rapid development of deep neural network in pro-K E Y W O R D S attention mechanism, binary similarity, graph neural network, program analysis | INTRODUCTIONBinary code similarity detection is a key foundation in maintaining software security, which can be generally applied in searching critical vulnerability 1 and detecting malware, 2 and so forth. Today, with the booming development of billions of Internet of Things (IoT) devices, the similarity analysis of binary code has become even more important than ever, because (1) code reuse is quite common in IoT which spreads a single vulnerability at source code level across thousands or more embedded devices that have diverse hardware architectures and software platforms 3 and (2) a known vulnerability that has been exploited in the past can also cause unprecedented damage and significant revenue losses to our daily life. 4 Worryingly, accurate and efficient analysis of stripped binary code is difficult because debug information such as symbol names, types, and location of commercial off-the-shelf (COTS) binary is unavailable. Besides, compared to binary code similarity detection on single-platform, function identification on cross-platform needs to deal with the binary code differences caused by various architectures, optimization levels, and compilers, which is even more challenging.Traditional binary code similarity detection approaches 5 adopt symbolic execution, constraint solving, or theorem prover to directly check binary code equivalence. However, these methods are inefficient especially when the number of function pairs is large, making them difficult to be utilized in large-scale applications. Graph matching-based methods use program flow diagrams to represent functions and detect similar functions. [6][7][8][9][10][11] These methods are usually inaccurate or their detection performance is limited by the efficiency of graph matching algorithms. 1,12 For instance, Genius 10 takes more than 1 week to construct their proposed codebook for only three software packages and the time complexity is quadratic in the number of training samples and linear in the cost of the bipartite graph matching algorithm.Recently, with the rapid development of deep learning and graph neural network (GNN), 13,14 graph matching and classification problems such as social network graph analytics 15 and chemical formula matching 13 can be solved well by transforming a representative graph to an embedding.

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Towards a Big Data Curated Benchmark of Inter-project Code Clones

Cited by 238 publications

References 12 publications

SourcererCC

SourcererCC

Intercomponent Dependency Issues in Software Ecosystems

Focus : Function clone identification on cross‐platform

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