Paper Info
Title
NIL: large-scale detection of large-variance clones
Abstract
ABSTRACTA code clone (in short, clone) is a code fragment that is identical or similar to other code fragments in source code. Clones generated by a large number of changes to copy-and-pasted code fragments are called large-variance (modifications are scattered) or large-gap (modifications are in one place) clones. It is difficult for general clone detection techniques to detect such clones and thus specialized techniques are necessary. In addition, with the rapid growth of software development, scalable clone detectors that can detect clones in large codebases are required. However, there are no existing techniques for quickly detecting large-variance or large-gap clones in large codebases. In this paper, we propose a scalable clone detection technique that can detect large-variance clones from large codebases and describe its implementation, called NIL. NIL is a token-based clone detector that efficiently identifies clone candidates using an N-gram representation of token sequences and an inverted index. Then, NIL verifies the clone candidates by measuring their similarity based on the longest common subsequence between their token sequences. We evaluate NIL in terms of large- variance clone detection accuracy, general Type-1, Type-2, and Type- 3 clone detection accuracy, and scalability. Our experimental results show that NIL has higher accuracy in terms of large-variance clone detection, equivalent accuracy in terms of general clone detection, and the shortest execution time for inputs of various sizes (1–250 MLOC) compared to existing state-of-the-art tools.
Year
DOI
Venue
2021
10.1145/3468264.3468564
Foundations of Software Engineering
Keywords
DocType
Citations 
Clone Detection, Large-Variance Clone, Scalability
Conference
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Tasuku Nakagawa100.34
Yoshiki Higo256046.90
Shinji Kusumoto31811137.88