  Programmers often reuse code from source code repositories to reduce the
development effort. Code clones are candidates for reuse in exploratory or
rapid development, as they represent often repeated functionality in software
systems. To facilitate code clone reuse, we propose DeepClone, a novel approach
utilizing a deep learning algorithm for modeling code clones to predict the
next set of tokens (possibly a complete clone method body) based on the code
written so far. The predicted tokens require minimal customization to fit the
context. DeepClone applies natural language processing techniques to learn from
a large code corpus, and generates code tokens using the model learned. We have
quantitatively evaluated our solution to assess (1) our model's quality and its
accuracy in token prediction, and (2) its performance and effectiveness in
clone method prediction. We also discuss various application scenarios for our
approach.
