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A Machine Learning Approach for Continuous Development

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Proceedings of 5th International Conference in Software Engineering for Defence Applications (SEDA 2016)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 717))

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Abstract

Complex and ephemeral software requirements, short time-to-market plans and fast changing information technologies have a deep impact on the design of software architectures, especially in Agile/DevOps projects where micro-services are integrated rapidly and incrementally. In this context, the ability to analyze new software requirements and understand very quickly and effectively their impact on the software architecture design becomes quite crucial. In this work we propose a novel and flexible approach for applying machine learning techniques to assist and speed-up the continuous development process, specifically within the mission-critical domain, where requirements are quite difficult to manage. More specifically, we introduce an Intelligent Software Assistant, designed as an open and plug-in based architecture powered by Machine Learning techniques and present a possible instantiation of this architecture in order to prove the viability of our solution.

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Acknowledgements

The Authors wish to thank the Consorzio Interuniversitario Nazionale per l’Informatica (CINI) and the Italian National Research Council (ISTC–CNR) for the partial financial support.

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Authors and Affiliations

  1. Department of Computer Science & Engineering, University of Bologna, Mura Anteo Zamboni, 7, 40126, Bologna, Italy

    Daniel Russo, Vincenzo Lomonaco & Paolo Ciancarini

Authors
  1. Daniel Russo
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  2. Vincenzo Lomonaco
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  3. Paolo Ciancarini
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Corresponding author

Correspondence to Daniel Russo .

Editor information

Editors and Affiliations

  1. University of Bologna, Bologna, Italy

    Paolo Ciancarini

  2. ‎Innopolis University, ‎Innopolis, Russia

    Stanislav Litvinov

  3. Innopolis University, Innopolis, Russia

    Angelo Messina

  4. Innopolis University, Innopolis, Russia

    Alberto Sillitti

  5. Innopolis University, Innopolis, Russia

    Giancarlo Succi

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Russo, D., Lomonaco, V., Ciancarini, P. (2018). A Machine Learning Approach for Continuous Development. In: Ciancarini, P., Litvinov, S., Messina, A., Sillitti, A., Succi, G. (eds) Proceedings of 5th International Conference in Software Engineering for Defence Applications. SEDA 2016. Advances in Intelligent Systems and Computing, vol 717. Springer, Cham. https://doi.org/10.1007/978-3-319-70578-1_11

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  • DOI: https://doi.org/10.1007/978-3-319-70578-1_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-70577-4

  • Online ISBN: 978-3-319-70578-1

  • eBook Packages: EngineeringEngineering (R0)

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