Fine-Grained Code Clone Detection by Keywords-Based Connection of Program Dependency Graph

Code clone detection is intended to identify functionally similar code fragments, a matter of escalating significance in contemporary software engineering. Numerous methodologies have been proffered for the detection of code clones, among which graph-based approaches exhibit efficacy in addressing semantic code clones. However, they all only consider the feature extraction of a single sample and ignore the semantic connection between different samples, resulting in the detection effect being unsatisfactory. Simultaneously, the majority of existing methods can only ascertain the presence of clones, lacking the capability to provide nuanced insights into which lines of code exhibit greater similarity. In this article, we advocate a novel PDG-based semantic clone detection method, namely, Keybor which can locate specific cloned lines of code by providing a fine-grained analysis of clone pairs. The highlight of the approach is to consider keywords as a bridge to connect PDG nodes of the target program to retain more semantic information about the functional code. To examine the effectiveness of Keybor, we assess it on a widely used BigCloneBench dataset. Experimental results indicate that Keybor is superior to 14 advanced code clone detection tools (i.e., CCAligner, SourcererCC, Siamese, NIL, NiCad, LVMapper, CCFinder, CloneWorks, Oreo, Deckard, CCGraph, Code2Img, GPT-3.5-turbo, and GPT-4).