Pengcheng Wang


DOI bib
LVMapper: A Large-Variance Clone Detector Using Sequencing Alignment Approach
Ming Wu, Pengcheng Wang, Kangqi Yin, Haoyu Cheng, Yun Xu, Chanchal K. Roy
IEEE Access, Volume 8

To detect large-variance code clones (i.e. clones with relatively more differences) in large-scale code repositories is difficult because most current tools can only detect almost identical or very similar clones. It will make promotion and changes to some software applications such as bug detection, code completion, software analysis, etc. Recently, CCAligner made an attempt to detect clones with relatively concentrated modifications called large-gap clones. Our contribution is to develop a novel and effective detection approach of large-variance clones to more general cases for not only the concentrated code modifications but also the scattered code modifications. A detector named LVMapper is proposed, borrowing and changing the approach of sequencing alignment in bioinformatics which can find two similar sequences with more differences. The ability of LVMapper was tested on both self-synthetic datasets and real cases, and the results show substantial improvement in detecting large-variance clones compared with other state-of-the-art tools including CCAligner. Furthermore, our new tool also presents good recall and precision for general Type-1, Type-2 and Type-3 clones on the widely used benchmarking dataset, BigCloneBench.


DOI bib
Pengcheng Wang, Jeffrey Svajlenko, Yanzhao Wu, Yun Xu, Chanchal K. Roy
Proceedings of the 40th International Conference on Software Engineering

Copying code and then pasting with large number of edits is a common activity in software development, and the pasted code is a kind of complicated Type-3 clone. Due to large number of edits, we consider the clone as a large-gap clone. Large-gap clone can reflect the extension of code, such as change and improvement. The existing state-of-the-art clone detectors suffer from several limitations in detecting large-gap clones. In this paper, we propose a tool, CCAligner, using code window that considers e edit distance for matching to detect large-gap clones. In our approach, a novel e-mismatch index is designed and the asymmetric similarity coefficient is used for similarity measure. We thoroughly evaluate CCAligner both for large-gap clone detection, and for general Type-1, Type-2 and Type-3 clone detection. The results show that CCAligner performs better than other competing tools in large-gap clone detection, and has the best execution time for 10MLOC input with good precision and recall in general Type-1 to Type-3 clone detection. Compared with existing state-of-the-art tools, CCAligner is the best performing large-gap clone detection tool, and remains competitive with the best clone detectors in general Type-1, Type-2 and Type-3 clone detection.