  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.
