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Application of Computational Linguistics Techniques for Improving Software Quality

  • Amin BoudeffaEmail author
  • Antonin Abherve
  • Alessandra Bagnato
  • Cedric Thomas
  • Martin Hamant
  • Assad Montasser
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11915)

Abstract

Progress in Artificial Intelligence, Big Data and Computational Linguistics domains offered new way to perform n-depth analysis and evidence-based quality assessments of open source software components. In this paper we will see how this can be integrated into industrial development to improve the quality of developed software.

Keywords

Computational Linguistics Big Data Sentiment analysis 

Notes

Acknowledgments

The research described has been carried out as part of the CROSSMINER Project, which has received funding from the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement No. 732223.

References

  1. 1.
    Boudeffa, A., Bagnato, A., Abherve, A., Di Ruscio, D., Mateus, M., Almeida, B.: Integrating and deploying heterogeneous components by means of a microservice architecture in the CROSSMINER project. STAF-CE 1(5), 61–66 (2019) Google Scholar
  2. 2.
    Bagnato, A., et al.: Developer-centric knowledge mining from large open-source software repositories (CROSSMINER). In: Seidl, M., Zschaler, S. (eds.) STAF 2017. LNCS, vol. 10748, pp. 375–384. Springer, Cham (2018).  https://doi.org/10.1007/978-3-319-74730-9_33CrossRefGoogle Scholar
  3. 3.
    Edge Hill University: D3.4 Natural Language Components, 27 December 2017 FinalGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Amin Boudeffa
    • 1
    Email author
  • Antonin Abherve
    • 1
  • Alessandra Bagnato
    • 1
  • Cedric Thomas
    • 2
  • Martin Hamant
    • 2
  • Assad Montasser
    • 2
  1. 1.SofteamParisFrance
  2. 2.OW2ParisFrance

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