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Evaluating clone detection tools with BigCloneBench
Abstract
Many clone detection tools have been proposed in the literature. However, our knowledge of their performance in real software systems is limited, particularly their recall. In this paper, we use our big data clone benchmark, BigCloneBench, to evaluate the recall of ten clone detection tools. BigCloneBench is a collection of eight million validated clones within IJaDataset-2.0, a big data software repository containing 25,000 open-source Java systems. BigCloneBench contains both intra-project and inter-project clones of the four primary clone types. We use this benchmark to evaluate the recall of the tools per clone type and across the entire range of clone syntactical similarity. We evaluate the tools for both single-system and cross-project detection scenarios. Using multiple clone-matching metrics, we evaluate the quality of the tools' reporting of the benchmark clones with respect to refactoring and automatic clone analysis use-cases. We compare these real-world results against our Mutation and Injection Framework, a synthetic benchmark, to reveal deeper understanding of the tools. We found that the tools have strong recall for Type-1 and Type-2 clones, as well as Type-3 clones with high syntactical similarity. The tools have weaker detection of clones with lower syntactical similarity.
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“leading a software repository usually containing 7-23% cloned codes (Svajlenko and Roy, 2015).”
From ReACC: A Retrieval-Augmented Code Completion Framework (2022) · cites this paper as Svajlenko and Roy, 2015
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An enhanced transformer-based framework for interpretable code clone detection
Nashaat, M., Amin, R., Eid, A. H., & Abdel-Kader, R. F. (2025) · Journal of Systems and Software · doi:10.1016/j.jss.2025.112347 · Cited by 9
Unlearnable Examples: Protecting Open-Source Software from Unauthorized Neural Code Learning
Ji, Z., Ma, P., & Wang, S. (2022) · Proceedings/Proceedings of the ... International Conference on Software Engineering and Knowledge Engineering · doi:10.18293/seke2022-066 · Cited by 7
Adding Context to Source Code Representations for Deep Learning
Tian, F., & Treude, C. (2022) · IEEE International Conference on Software Maintenance (ICSM) · doi:10.1109/icsme55016.2022.00042 · Cited by 9
Enhancing Source Code Representations for Deep Learning with Static Analysis
Guan, X., & Treude, C. (2024) · IEEE International Conference on Program Comprehension (ICPC) · doi:10.1145/3643916.3644396 · Cited by 7
Assessing and Improving an Evaluation Dataset for Detecting Semantic Code Clones via Deep Learning
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Metadata
- Authors
- Svajlenko, J., & Roy, C. K.
- Affiliations
- University of Saskatchewan (CA)
- Venue
- 2015 IEEE International Conference on Software Maintenance and Evolution (ICSME)
- Publisher
- IEEE
- Published
- 2015-09
- Pages
- 131-140
- ISSN
- 1063-6773 (ISSN-L)
- OpenAlex
- W2158439356
- Cited by
- 172
- References
- 28
- Citations by year
- 2026: 7 · 2025: 16 · 2024: 10 · 2023: 16 · 2022: 22 · 2021: 28 · 2020: 21 · 2019: 15 · 2018: 17 · 2017: 9 · 2016: 10 · 2015: 1
- FWCI
- 10.72
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE International Conference on Software Maintenance (ICSM) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Information Systems › Software Engineering Research
- Physical Sciences › Computer Science › Software › Software Testing and Debugging Techniques
- Physical Sciences › Computer Science › Software › Advanced Software Engineering Methodologies
- Keywords
- clone detection, BigCloneBench, memory retrieval, refactoring, synthetic benchmark
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