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An End-to-End Framework for Productive Use of Machine Learning in Software Analytics and Business Intelligence Solutions

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Product-Focused Software Process Improvement (PROFES 2020)

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

Abstract

Nowadays, machine learning (ML) is an integral component in a wide range of areas, including software analytics (SA) and business intelligence (BI). As a result, the interest in custom ML-based software analytics and business intelligence solutions is rising. In practice, however, such solutions often get stuck in a prototypical stage because setting up an infrastructure for deployment and maintenance is considered complex and time-consuming. For this reason, we aim at structuring the entire process and making it more transparent by deriving an end-to-end framework from existing literature for building and deploying ML-based software analytics and business intelligence solutions. The framework is structured in three iterative cycles representing different stages in a model’s lifecycle: prototyping, deployment, update. As a result, the framework specifically supports the transitions between these stages while also covering all important activities from data collection to retraining deployed ML models. To validate the applicability of the framework in practice, we compare it to and apply it in a real-world ML-based SA/BI solution.

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Notes

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  2. 2.

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  3. 3.

    https://aws.amazon.com/sagemaker/.

  4. 4.

    https://aws.amazon.com/quicksight/.

  5. 5.

    https://docs.aws.amazon.com/step-functions/latest/dg/concepts-python-sdk.html.

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Author information

Authors and Affiliations

  1. Corporate Technology, Siemens AG, 81739, Munich, Germany

    Iris Figalist & Christoph Elsner

  2. Department of Computer Science and Engineering, Chalmers University of Technology, Hörselgången 11, 412 96, Göteborg, Sweden

    Jan Bosch

  3. Department of Computer Science and Media Technology, Malmö University, Nordenskiöldsgatan, 211 19, Malmö, Sweden

    Helena Holmström Olsson

Authors
  1. Iris Figalist
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  2. Christoph Elsner
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  3. Jan Bosch
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  4. Helena Holmström Olsson
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Corresponding author

Correspondence to Iris Figalist .

Editor information

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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Figalist, I., Elsner, C., Bosch, J., Olsson, H.H. (2020). An End-to-End Framework for Productive Use of Machine Learning in Software Analytics and Business Intelligence Solutions. 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_14

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  • DOI: https://doi.org/10.1007/978-3-030-64148-1_14

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-64147-4

  • Online ISBN: 978-3-030-64148-1

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