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Intelligent Code Completion

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Data Science and Computational Intelligence (ICInPro 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1483))

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Abstract

Auto complete suggestions for IDEs are widely used and often extremely helpful for inexperienced and expert developers alike. This paper proposes and illustrates an intelligent code completion system using an LSTM based Seq2Seq model that can be used in concert with traditional methods (Such as static analysis, prefix filtering, and tries) to increase the effectiveness of auto complete suggestions and help accelerate coding.

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References

  1. Intelligent code completion (2021). https://en.wikipedia.org/wiki/Intelligent_code_completion

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Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, National Institute of Technology Karnataka, Surathkal, Mangalore, India

    Danish Waseem,  Pintu & B. R. Chandavarkar

Authors
  1. Danish Waseem
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  2. Pintu
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  3. B. R. Chandavarkar
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Corresponding author

Correspondence to Danish Waseem .

Editor information

Editors and Affiliations

  1. Bangalore University, Bengaluru, India

    K. R. Venugopal

  2. Bangalore University, Bengaluru, India

    P. Deepa Shenoy

  3. University of Melbourne, Melbourne, VIC, Australia

    Rajkumar Buyya

  4. NIAS, Bengaluru, India

    L. M. Patnaik

  5. Florida International University, Miami, FL, USA

    Sitharama S. Iyengar

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Waseem, D., Pintu, Chandavarkar, B.R. (2021). Intelligent Code Completion. In: Venugopal, K.R., Shenoy, P.D., Buyya, R., Patnaik, L.M., Iyengar, S.S. (eds) Data Science and Computational Intelligence. ICInPro 2021. Communications in Computer and Information Science, vol 1483. Springer, Cham. https://doi.org/10.1007/978-3-030-91244-4_5

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  • DOI: https://doi.org/10.1007/978-3-030-91244-4_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-91243-7

  • Online ISBN: 978-3-030-91244-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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