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Source code clones are categorized into four types of increasing difficultyof 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, whichaccounts for syntactic differences. In between Type-3 and Type-4, however,there lies a spectrum of clones that, although still exhibiting some syntacticsimilarities, are extremely hard to detect -- the Twilight Zone. Most clonedetectors reported in the literature fail to operate in this zone. We presentOreo, a novel approach to source code clone detection that not only detectsType-1 to Type-3 clones accurately, but is also capable of detectingharder-to-detect clones in the Twilight Zone. Oreo is built using a combinationof machine learning, information retrieval, and software metrics. We evaluatethe recall of Oreo on BigCloneBench, and perform manual evaluation forprecision. Oreo has both high recall and precision. More importantly, it pushesthe boundary in detection of clones with moderate to weak syntactic similarityin a scalable manner.

Oreo: Detection of Clones in the Twilight Zone