DeepClone: Modeling Clones to Generate Code Predictions

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 app...

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Bibliographic Details
Published in:arXiv.org 2020-12
Main Authors: Hammad, Muhammad, Babur, Önder, Hamid Abdul Basit, van den Brand, Mark
Format: Article
Language:English
Subjects:
Algorithms
Building codes
Machine learning
Model accuracy
Modelling
Natural language processing
Quality assessment
Software development
Source code
Online Access:Get full text
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Summary: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.
ISSN:2331-8422