Oreo: Detection of Clones in the Twilight Zone

TL;DR

Oreo is a novel clone detection approach that effectively identifies simple to complex code clones, including those in the difficult Twilight Zone, using machine learning, information retrieval, and software metrics.

Contribution

Oreo introduces a scalable method capable of detecting code clones from Type-1 to the challenging Twilight Zone, surpassing existing tools in identifying weak syntactic similarities.

Findings

High recall on BigCloneBench dataset

Manual evaluation confirms high precision

Effective detection of clones in the Twilight Zone

Abstract

Source code clones are categorized into four types of increasing difficulty of detection, ranging from purely textual (Type-1) to purely semantic (Type-4). Most clone detectors reported in the literature work well up to Type-3, which accounts for syntactic differences. In between Type-3 and Type-4, however, there lies a spectrum of clones that, although still exhibiting some syntactic similarities, are extremely hard to detect -- the Twilight Zone. Most clone detectors reported in the literature fail to operate in this zone. We present Oreo, a novel approach to source code clone detection that not only detects Type-1 to Type-3 clones accurately, but is also capable of detecting harder-to-detect clones in the Twilight Zone. Oreo is built using a combination of machine learning, information retrieval, and software metrics. We evaluate the recall of Oreo on BigCloneBench, and perform manual evaluation for precision. Oreo has both high recall and precision. More importantly, it pushes the boundary in detection of clones with moderate to weak syntactic similarity in a scalable manner.