DeepClone: Modeling Clones to Generate Code Predictions

TL;DR

DeepClone leverages deep learning to model code clones, enabling accurate prediction of subsequent code tokens and facilitating code reuse in software development.

Contribution

It introduces a novel deep learning approach for modeling code clones to predict code tokens, aiding in code reuse and clone method generation.

Findings

High accuracy in token prediction

Effective clone method generation

Potential for reducing development effort

Abstract

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.