The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
CP-Miner: finding copy-paste and related bugs in large-scale software code
Abstract
Recent studies have shown that large software suites contain significant amounts of replicated code. It is assumed that some of this replication is due to copy-and-paste activity and that a significant proportion of bugs in operating systems are due to copy-paste errors. Existing static code analyzers are either not scalable to large software suites or do not perform robustly where replicated code is modified with insertions and deletions. Furthermore, the existing tools do not detect copy-paste related bugs. In this paper, we propose a tool, CP-Miner, that uses data mining techniques to efficiently identify copy-pasted code in large software suites and detects copy-paste bugs. Specifically, it takes less than 20 minutes for CP-Miner to identify 190,000 copy-pasted segments in Linux and 150,000 in FreeBSD. Moreover, CP-Miner has detected many new bugs in popular operating systems, 49 in Linux and 31 in FreeBSD, most of which have since been confirmed by the corresponding developers and have been rectified in the following releases. In addition, we have found some interesting characteristics of copy-paste in operating system code. Specifically, we analyze the distribution of copy-pasted code by size (number lines of code), granularity (basic blocks and functions), and modification within copy-pasted code. We also analyze copy-paste across different modules and various software versions.
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Cites
Schleimer, S., S, W. D., & Aiken, A. (2003) · Proceedings of the 2003 ACM SIGMOD International Conference on Management of Data · doi:10.1145/872757.872770 · Cited by 1,099 · cited 2×
“Index Terms—Software analysis, code reuse, code duplication, debugging aids, data mining.”
Background
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Retrieval-based neural source code summarization
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Deep learning similarities from different representations of source code
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A comparison of code similarity analysers
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Detecting differences across multiple instances of code clones
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Does BLEU Score Work for Code Migration?
Tran, N., Tran, H., Nguyen, S., Nguyen, H., & Nguyen, T. (2019) · 2019 IEEE/ACM 27th International Conference on Program Comprehension (ICPC) · doi:10.1109/icpc.2019.00034 · Cited by 49
AuDeNTES: Automatic Detection of teNtative plagiarism according to a rEference Solution
Mariani, L., & Micucci, D. (2012) · ACM Transactions on Computing Education · doi:10.1145/2133797.2133799 · Cited by 12
PaReco: patched clones and missed patches among the divergent variants of a software family
Ramkisoen, P. K., Businge, J., van Bladel, B., Decan, A., Demeyer, S., De Roover, C., & Khomh, F. (2022) · Proceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering · doi:10.1145/3540250.3549112 · Cited by 10
Metadata
- Authors
- Li, Z., Lu, S., Myagmar, S., & Zhou, Y.
- Venue
- IEEE Transactions on Software Engineering
- Published
- 2006-03
- Volume
- 32
- Issue
- 3
- Pages
- 176-192
- ISSN
- 0098-5589 (ISSN-L)
- OpenAlex
- W2104301886
- Cited by
- 610
- References
- 1
- FWCI
- 84.76
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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