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Context: Code Clone Detection (CCD) is a software engineering task that isused for plagiarism detection, code search, and code comprehension. Recently,deep learning-based models have achieved an F1 score (a metric used to assessclassifiers) of $\sim$95\% on the CodeXGLUE benchmark. These models requiremany training data, mainly fine-tuned on Java or C++ datasets. However, noprevious study evaluates the generalizability of these models where a limitedamount of annotated data is available.  Objective: The main objective of this research is to assess the ability ofthe CCD models as well as few shot learning algorithms for unseen programmingproblems and new languages (i.e., the model is not trained on theseproblems/languages).  Method: We assess the generalizability of the state of the art models for CCDin few shot settings (i.e., only a few samples are available for fine-tuning)by setting three scenarios: i) unseen problems, ii) unseen languages, iii)combination of new languages and new problems. We choose three datasets ofBigCloneBench, POJ-104, and CodeNet and Java, C++, and Ruby languages. Then, weemploy Model Agnostic Meta-learning (MAML), where the model learns ameta-learner capable of extracting transferable knowledge from the train set;so that the model can be fine-tuned using a few samples. Finally, we combinecontrastive learning with MAML to further study whether it can improve theresults of MAML.

Evaluating few shot and Contrastive learning Methods for Code Clone Detection