Finding file clones in FreeBSD Ports Collection

Sasaki, Yusuke; Yamamoto, Takakazu; Hayase, Yasuhiro; Inoue, Katsuro

doi:10.1109/msr.2010.5463293

Cited by 26 publications

(20 citation statements)

References 8 publications

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“…For example, Ossher et al [25] and Schwarz et al [31] use clone detection to determine the prevalence of reuse within open source development community. Inoue et al [30] use largescale clone detection for studying the evolution of cloned files across similar software systems. Koschke [21] studies the application of large-scale inter-system clone detection for license infringement detection.…”

Section: Introductionmentioning

confidence: 99%

Threshold-free code clone detection for a large-scale heterogeneous Java repository

Keivanloo

Zhang

Zou

2015

2015 IEEE 22nd International Conference on Software Analysis, Evolution, and Reengineering (SANER)

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Code clones are unavoidable entities in software ecosystems. A variety of clone-detection algorithms are available for finding code clones. For Type-3 clone detection at method granularity (i.e., similar methods with changes in statements), dissimilarity threshold is one of the possible configuration parameters. Existing approaches use a single threshold to detect Type-3 clones across a repository. However, our study shows that to detect Type-3 clones at method granularity on a large-scale heterogeneous repository, multiple thresholds are often required. We find that the performance of clone detection improves if selecting different thresholds for various groups of clones in a heterogeneous repository (i.e., various applications). In this paper, we propose a threshold-free approach to detect Type-3 clones at method granularity across a large number of applications. Our approach uses an unsupervised learning algorithm, i.e., k-means, to determine true and false clones. We use a clone benchmark with 330,840 tagged clones from 24,824 open source Java projects for our study. We observe that our approach improves the performance significantly by 12% in terms of Fmeasure. Furthermore, our threshold-free approach eliminates the concern of practitioners about possible misconfiguration of Type-3 clone detection tools.

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Section: Introductionmentioning

confidence: 99%

Threshold-free code clone detection for a large-scale heterogeneous Java repository

Keivanloo

Zhang

Zou

2015

2015 IEEE 22nd International Conference on Software Analysis, Evolution, and Reengineering (SANER)

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“…Sasaki et al proposed an approach that detects identical files and then investigate the characteristics of them [18]. This study identifies identical files without any (or just slight) modifications in comments or headers using MD5 hash values of the tokenized files.…”

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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“…Usually, software clones come into existence when a developer decides to copy-paste a code snippet from elsewhere in the system that solves a problem that is highly similar to the one under consideration. Empirical studies show that software clones often comprise 10%-20% of total source code base for large software systems of many application domains, such as operating systems [42], development environments [37], and database systems [40]. Code duplication offers developers a shortcut to reuse an existing design.…”

Section: Introductionmentioning

confidence: 99%

Recommending Clones for Refactoring Using Design, Context, and History

Wang

Godfrey

2014

2014 IEEE International Conference on Software Maintenance and Evolution

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Abstract-Developers know that copy-pasting code (aka code cloning) is often a convenient shortcut to achieving a design goal, albeit one that carries risks to the code quality over time. However, deciding which, if any, clones should be eliminated within an existing system is a daunting task. "Fixing" a clone usually means performing an invasive refactoring, and not all clones may be worth the effort, cost, and risk that such a change entails. Furthermore, sometimes cloning fulfils a useful design role, and should not be refactored at all [23]. And clone detection tools often return very large result sets, making it hard to choose which clones should be investigated and possibly removed.In this paper, we propose an automated approach to recommend clones for refactoring by training a decision tree-based classifier. We analyze more than 600 clone instances in three medium-to large-sized open source projects, and we collect features that are associated with the source code, the context, and the history of clone instances. Our approach achieves a precision of around 80% in recommending clone refactoring instances for each target system, and similarly good precision is achieved in cross-project evaluation. By recommending which clones are appropriate for refactoring, our approach allows for better resource allocation for refactoring itself after obtaining clone detection results, and can thus lead to improved clone management in practice.

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Finding file clones in FreeBSD Ports Collection

Cited by 26 publications

References 8 publications

Threshold-free code clone detection for a large-scale heterogeneous Java repository

Threshold-free code clone detection for a large-scale heterogeneous Java repository

Proposing and Evaluating Clone Detection Approaches with Preprocessing Input Source Files

Recommending Clones for Refactoring Using Design, Context, and History

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