Using Dynamic Time Warping to Detect Clones in Software Systems

Using Dynamic Time Warping to Detect Clones in Software Systems

Mostefai Abdelkader
Copyright: © 2021 |Pages: 17
DOI: 10.4018/IJSI.2021010103
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Abstract

Software clone detection is a widely researched area over the last two decades. Code clones are fragments of code judged similar by some metric of similarity. This paper proposes an approach for code clone detection using dynamic time warping technique (i.e., DTW). DTW is a well-known algorithm for aligning and measuring similarity of time series and it has been found effective in many domains where similarity plays an important role such as speech and gesture recognition. The proposed approach finds clones in three steps. First software modules are extracted. Then, the extracted modules are turned to time series. Finally, the time series are compared using the DTW algorithm to find clones. The results of the experiment conducted on a well-known Benchmark show that the approach can detect clones effectively in software systems.
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Many methods, techniques and tools have been proposed for software clone detection.

Lingxiao et al (2005) proposed an efficient algorithm for detecting similar subtrees from the tree representing the source code. First, The algorithm maps subtrees to numerical vectors. Then a clustering algorithm partitions these vectors into a set of clusters where each cluster contains similar subtrees. The experimentation done using the DECKARD tool showed that the approach is scalable and accurate.

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