Problems creating task-relevant clone detection reference data

Walenstein, Andrew; Jyoti, N.; Li, Junwei; Yang, Yun; Lakhotia, Arun

doi:10.1109/wcre.2003.1287259

Cited by 38 publications

(36 citation statements)

References 14 publications

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“…Duplicates can also be used to keep unreadable, complicated abstractions from entering the system. Lack of expressiveness of a given programming language may lead to the use of "boiler-plated" solutions for particular problems [30], or even source code generation. This kind of technique is common in COBOL development, for example.…”

Section: Code Cloningmentioning

confidence: 99%

"Cloning Considered Harmful" Considered Harmful

Kapser

Godfrey

2006

2006 13th Working Conference on Reverse Engineering

185

125

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Section: Code Cloningmentioning

confidence: 99%

"Cloning Considered Harmful" Considered Harmful

Kapser

Godfrey

2006

2006 13th Working Conference on Reverse Engineering

185

125

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“…Manually inspecting a system for clones is non-trivial. Even a small system like Cook, when considering only function clones, has almost a million function pairs to inspect [37]. Bellon et al [4] created a benchmark by validating clones reported by the clone detectors themselves.…”

Section: Recallmentioning

confidence: 99%

SourcererCC

Sajnani

Saini

Svajlenko

et al. 2016

Proceedings of the 38th International Conference on Software Engineering

355

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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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“…Based on the guidelines, 10 judges carefully reviewed the revision logs and function pairs, and determined if each function pair has an origin relationship. The judging process is similar to that used for finding code clone detection by human judges in [23]. All human judges have strong C programming experience, and are graduate students in the computer science department.…”

Section: Origin Relationship Manual Identification Guidelinesmentioning

confidence: 99%

“…This is more complex than it seems. Wallenstein et al have shown that human judges never completely agree in their assessments [23].…”

mentioning

confidence: 99%