DeepClone: Modeling Clones to Generate Code Predictions
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by
Muhammad Hammad, Önder Babur, Hamid Abdul Basit, Mark van den Brand
2020
Abstract
During software development, programmers often tend to reuse the code for
common functionalities, available in other source code repositories. This
activity helps them to reduce time and effort to develop code, instead of
building it from scratch. Code clones are candidates for reuse in an
exploratory or rapid development, as they represent often repeated
functionality in software systems. To facilitate code clone reuse, we propose a
novel approach, Deep-Clone, where we utilize a deep learning algorithm for
modeling code clones and predicting the next possible set of tokens (up to the
cloned method body) based on the user input so far. The generated predictions
aim to potentially help developers to write code rapidly with minimum tuning of
values later on. DeepClone applies natural language processing techniques to
learn from a large code corpus (the BigCloneBench dataset), and generates code
tokens (full clone methods where applicable) 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. With a high quality and accurate model as the
foundation, we further discuss scenarios for exploiting our approach.
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