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From a Data Science Driven Process to a Continuous Delivery Process for Machine Learning Systems

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Product-Focused Software Process Improvement (PROFES 2020)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 12562))

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Abstract

Development of machine learning (ML) enabled applications in real-world settings is challenging and requires the consideration of sound software engineering (SE) principles and practices. A large body of knowledge exists on the use of modern approaches to developing traditional software components, but not ML components. Using exploratory case study approach, this study investigates the adoption and use of existing software development approaches, specifically continuous delivery (CD), to development of ML components. Research data was collected using a multivocal literature review (MLR) and focus group technique with ten practitioners involved in developing ML-enabled systems at a large telecommunication company. The results of our MLR show that companies do not outright apply CD to the development of ML components rather as a result of improving their development practices and infrastructure over time. A process improvement conceptual model, that includes the description of CD application to ML components is developed and initially validated in the study.

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Notes

  1. 1.

    By AI-enabled systems we mean the software systems that include ML components.

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Acknowledgement

This research was supported by Software Center, Chalmers AI Research Centre (CHAIR), and Vinnova project HoliDev. The authors would also like to thank all the participants of focus group discussions.

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Authors and Affiliations

  1. Chalmers University of Technology, Gothenburg University, Gothenburg, Sweden

    Lucy Ellen Lwakatare, Ivica Crnkovic & Jan Bosch

  2. Ericsson, Gothenburg, Sweden

    Ellinor Rånge

Authors
  1. Lucy Ellen Lwakatare
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  2. Ivica Crnkovic
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  3. Ellinor Rånge
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  4. Jan Bosch
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Corresponding author

Correspondence to Lucy Ellen Lwakatare .

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Editors and Affiliations

  1. Politecnico di Torino, Turin, Torino, Italy

    Maurizio Morisio

  2. Polytechnic University of Turin, Turin, Torino, Italy

    Marco Torchiano

  3. Fraunhofer Institute for Experimental Software Engineering, Kaiserslautern, Rheinland-Pfalz, Germany

    Andreas Jedlitschka

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Lwakatare, L.E., Crnkovic, I., Rånge, E., Bosch, J. (2020). From a Data Science Driven Process to a Continuous Delivery Process for Machine Learning Systems. In: Morisio, M., Torchiano, M., Jedlitschka, A. (eds) Product-Focused Software Process Improvement. PROFES 2020. Lecture Notes in Computer Science(), vol 12562. Springer, Cham. https://doi.org/10.1007/978-3-030-64148-1_12

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  • DOI: https://doi.org/10.1007/978-3-030-64148-1_12

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