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Deep Learning-Based Code Auto-Completion for Distributed Applications

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Distributed Computing and Artificial Intelligence, 19th International Conference (DCAI 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 583))

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Abstract

Distributed computing has been gaining a continually increasing interest over the past years in research and industrial communities. One of the significant objectives of distributed computing is to provide the infrastructure for performing tasks on independent systems. Utilizing this approach in software development can reduce costs. Consequently, there has been an increasing interest in distributed applications. However, distributed applications need to meet main features, such as scalability, availability, and compatibility. In this context, service-based systems provide an architecture that can support mentioned features. Nevertheless, current services use various technologies and languages, which bring complexity to development. This work aims to facilitate web service development by introducing a deep Learning-based code auto-complete model. This model is used in the toolkit called SmartCLIDE, which provides features to accelerate development using Artificial Intelligence and cloud deployment. The contribution of this work can fall into two steps: First, the top web APIs from a benchmark web service data-set has been identified. Afterward, a data optimization approach has been proposed to systematically augment and improve available web service codes. Second, the service code auto-completion model has been trained, which takes advantage of text generation trends and deep learning methods. The experimental results on web service codes demonstrate that the proposed approach outperforms another general-purpose code-completion model.

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Acknowledgments

This research has been supported by the European Union’s Horizon 2020 research and innovation programme under grant agreement No 871177—This work has been supported by the Institute for Business Competitiveness of Castilla y León, and the European Regional Development Fund under grant CCTT3/20/SA/0002 (AIR-SCity project).

Author information

Authors and Affiliations

  1. Air Institute, IoT Digital Innovation Hub, Carbajosa de la Sagrada, 37188, Salamanca, Spain

    Zakieh Alizadehsani, David Alonso-Moro, David Berrocal Macías & Alfonso González-Briones

  2. BISITE Research Group, University of Salamanca. Edificio Multiusos I+D+i, 37007, Salamanca, Spain

    Francisco Pinto-Santos, David Alonso-Moro & Alfonso González-Briones

  3. Computer Science Faculty, Pontifical University of Salamanca, C/Compañia, 5, 37002, Salamanca, Spain

    Francisco Pinto-Santos

Authors
  1. Zakieh Alizadehsani
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  2. Francisco Pinto-Santos
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  3. David Alonso-Moro
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  4. David Berrocal Macías
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  5. Alfonso González-Briones
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Corresponding author

Correspondence to Zakieh Alizadehsani .

Editor information

Editors and Affiliations

  1. Hiroshima University, Hiroshima, Japan

    Sigeru Omatu

  2. King Abdulaziz University, Jeddah, Saudi Arabia

    Rashid Mehmood

  3. Kielce University of Technology, Kielce, Poland

    Pawel Sitek

  4. Palazzo Camponeschi, University of L'Aquila, L'Aquila, Italy

    Serafino Cicerone

  5. BISITE, Edificio I+D+i, University of Salamanca, Salamanca, Spain

    Sara Rodríguez

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Alizadehsani, Z., Pinto-Santos, F., Alonso-Moro, D., Macías, D.B., González-Briones, A. (2023). Deep Learning-Based Code Auto-Completion for Distributed Applications. In: Omatu, S., Mehmood, R., Sitek, P., Cicerone, S., Rodríguez, S. (eds) Distributed Computing and Artificial Intelligence, 19th International Conference. DCAI 2022. Lecture Notes in Networks and Systems, vol 583. Springer, Cham. https://doi.org/10.1007/978-3-031-20859-1_14

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