CCT-Code: Cross-Consistency Training for Multilingual Clone Detection
  and Code Search
Nikita Sorokin Dmitry Abulkhanov Sergey Nikolenko Valentin Malykh
We consider the clone detection and information retrieval problems for source
code, well-known tasks important for any programming language. Although it is
also an important and interesting problem to find code snippets that operate
identically but are written in different programming languages, to the best of
our knowledge multilingual clone detection has not been studied in literature.
In this work, we formulate the multilingual clone detection problem and present
XCD, a new benchmark dataset produced from the CodeForces submissions dataset.
Moreover, we present a novel training procedure, called cross-consistency
training (CCT), that we apply to train language models on source code in
different programming languages. The resulting CCT-LM model, initialized with
GraphCodeBERT and fine-tuned with CCT, achieves new state of the art,
outperforming existing approaches on the POJ-104 clone detection benchmark with
95.67\% MAP and AdvTest code search benchmark with 47.18\% MRR; it also shows
the best results on the newly created multilingual clone detection benchmark
XCD across all programming languages.