The DOI matches its Crossref record.
The NiCad Clone Detector
Abstract
The NiCad Clone Detector is a scalable, flexible clone detection tool designed to implement the NiCad (Automated Detection of Near-Miss Intentional Clones) hybrid clone detection method in a convenient, easy-to-use command-line tool that can easily be embedded in IDEs and other environments. It takes as input a source directory or directories to be checked for clones and a configuration file specifying the normalization and filtering to be done, and provides output results in both XML form for easy analysis and HTML form for convenient browsing. NiCad handles a range of languages and normalizations, and is designed to be easily extensible using a component-based plugin architecture. It is scalable to very large systems and has been used to analyze, for example, all 47 releases of FreeBSD (60 million lines) as a single system.
Overview
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Citations
Cited by
(2013) · doi:10.5555/2662708.2662710 · Cited by 34
JEMMA: An extensible Java dataset for ML4Code applications
Karmakar, A., Allamanis, M., & Robbes, R. (2023) · Empirical Software Engineering · doi:10.1007/s10664-022-10275-7 · Cited by 4
Metadata
- Authors
- Cordy, J. R., & Roy, C. K.
- Venue
- 2011 IEEE 19th International Conference on Program Comprehension
- Publisher
- IEEE
- Published
- 2011-06
- Pages
- 219-220
- OpenAlex
- W2162436321
- Cited by
- 256
- References
- 0
- FWCI
- 10.10
- Type
- conference paper
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
