  Software developers frequently reuse source code from repositories as it
saves development time and effort. Code clones accumulated in these
repositories hence represent often repeated functionalities and are candidates
for reuse in an exploratory or rapid development. In previous work, we
introduced DeepClone, a deep neural network model trained by fine tuning GPT-2
model over the BigCloneBench dataset to predict code clone methods. The
probabilistic nature of DeepClone output generation can lead to syntax and
logic errors that requires manual editing of the output for final reuse. In
this paper, we propose a novel approach of applying an information retrieval
(IR) technique on top of DeepClone output to recommend real clone methods
closely matching the predicted output. We have quantitatively evaluated our
strategy, showing that the proposed approach significantly improves the quality
of recommendation.
