CP-Miner: A Tool for Finding Copy-paste and Related Bugs in Operating System Code

By Zhenmin Li, Shan Lu, Suvda Myagmar, Yuanyuan Zhou. USENIX Symposium on Operating Systems Design and Implementation, 2004

395 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/05c106bb1a21a8d8d99b76953231f1476ec73df2

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Citations in the corpus

CP-Miner: finding copy-paste and related bugs in large-scale software code (2006, methodology): “An earlier version of this paper [1] appeared in the Proceedings of the Sixth Symposium on Operating System Design and Implementation.”
A Study of "Wheat" and "Chaff" in Source Code (2015, result): “This is consistent with other research [3, 15] that also ignores shorter methods.”
Survey of Research on Software Clones (2006, background): “[55] cast the search for similar fragments as a data mining problem.”
A Survey on Software Clone Detection Research (2007, methodology): “The tendency of cloning not only produces code that is difficult to maintain, but may also introduce subtle errors [51, 168, 169].”
Detecting large-scale system problems by mining console logs (2009, background): “, 2009), as well as pinpointing application bugs (Li et al., 2004; Zheng et al., 2006) and configuration problems (Wang et al.”
Scalable detection of semantic clones (2008, background): “D ECKARD [9], CP-Miner [14], CCFinder [10], and CloneDR [3] represent the most mature clone detection techniques.”
Scalable Graph-based Bug Search for Firmware Images (2016, methodology): “Similarly, token-based approaches such as CCFinder [33] and CP-Miner [35] utilize token sequence and scan for duplicate token sequences in other source code.”
Frequent pattern mining: current status and future directions (2007, methodology): “Based on CloSpan (Yan et al. 2003) an algorithm called C-Miner , by Li et al. (2004), was proposed to mine frequent sequential patterns of correlated blocks from block access traces.”
Comparison and Evaluation of Clone Detection Tools (2007, unknown): “Ç”
Achieving accuracy and scalability simultaneously in detecting application clones on Android markets (2014, background): “Token-based approaches [40, 28, 29, 22] parse a program to a sequence of tokens and compare these tokens to find clones.”
discovRE: Efficient Cross-Architecture Identification of Bugs in Binary Code (2016, methodology): “Token-based approaches such as CCFinder [38] and CP-Miner [43] analyze the token sequence produced by lexer and scan for duplicate token subsequences, which indicate potential code clones.”
PR-Miner: automatically extracting implicit programming rules and detecting violations in large software code (2005, methodology): “In order to eliminate the propagation effect, we can combine with our previous work on copy-paste detection called CP-Miner [18], which also works with large software.”
Have things changed now?: an empirical study of bug characteristics in modern open source software (2006, background): “Recently, software and its development process have significantly changed in many ways, including more help from bug detection tools, shift towards multi-threading architecture, the opensource development paradigm and increasing concerns about security and user-friendly interface.”
Learning from mistakes: a comprehensive study on real world concurrency bug characteristics (2008, unknown): “A recent study of operating system bugs [8] revealed that copy-paste was an important cause of semantic bugs, and has inspired a tool called CP-Miner that focused on detecting copy-pasted code and semantic bugs related to copy-paste [19].”
DeepSim: deep learning code functional similarity (2018, methodology): “3236068 code search and reuse [23, 30], refactoring [20, 61], bug detection [27, 36].”