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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Clone-advisor: recommending code tokens and clone methods with deep learning and information retrieval.

Muhammad Hammad1, Önder Babur1,2, Hamid Abdul Basit3

  • 1Eindhoven University of Technology, Eindhoven, Netherlands.

Peerj. Computer Science
|December 15, 2021
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Summary
This summary is machine-generated.

This study introduces Clone-Advisor, enhancing DeepClone

Keywords:
Code cloneCode predictionCode searchDeep learningInformation retrievalLanguage modeling

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Area of Science:

  • Software Engineering
  • Artificial Intelligence
  • Machine Learning

Background:

  • Software developers reuse code from repositories to save time.
  • Code clones, or similar code fragments, represent reusable functionalities.
  • Previous work introduced DeepClone for predicting code clones using deep learning.

Purpose of the Study:

  • To improve the accuracy and reliability of code clone prediction.
  • To address syntax and logic errors in deep learning-based code generation.
  • To refine and evaluate the DeepClone model and introduce Clone-Advisor.

Main Methods:

  • Developed DeepClone, a deep learning model for code clone prediction.
  • Integrated an information retrieval technique with DeepClone to create Clone-Advisor.
  • Quantitatively evaluated the performance of Clone-Advisor for clone method recommendation.

Main Results:

  • DeepClone models code clones and non-cloned code for prediction.
  • Clone-Advisor recommends real clone methods to refine DeepClone's output.
  • The proposed Clone-Advisor improves upon the original DeepClone predictions.

Conclusions:

  • Clone-Advisor effectively recommends relevant code clones, enhancing developer productivity.
  • The refined DeepClone model and Clone-Advisor offer a robust solution for code reuse.
  • This approach mitigates common errors in probabilistic language modeling for code.