Cited by 241Software Engineering ResearchSoftware Engineering Techniques and PracticesAdvanced Software Engineering Methodologies

Large-scale machine learning systems in real-world industrial settings: A review of challenges and solutions

Lwakatare, Lucy Ellen, Raj, Aiswarya, Crnkovic, Ivica, Bosch, Jan, Olsson, Helena Holmström·2020·Information and Software Technology

Abstract

Background: Developing and maintaining large scale machine learning (ML) based software systems in an industrial setting is challenging. There are no well-established development guidelines, but the literature contains reports on how companies develop and maintain deployed ML-based software systems. Objective: This study aims to survey the literature related to development and maintenance of large scale ML-based systems in industrial settings in order to provide a synthesis of the challenges that practitioners face. In addition, we identify solutions used to address some of these challenges. Method: A systematic literature review was conducted and we identified 72 papers related to development and maintenance of large scale ML-based software systems in industrial settings. The selected articles were qualitatively analyzed by extracting challenges and solutions. The challenges and solutions were thematically synthesized into four quality attributes: adaptability, scalability, safety and privacy. The analysis was done in relation to ML workflow, i.e. data acquisition, training, evaluation, and deployment. Results: We identified a total of 23 challenges and 8 solutions related to development and maintenance of large scale ML-based software systems in industrial settings including six different domains. Challenges were most often reported in relation to adaptability and scalability. Safety and privacy challenges had the least reported solutions. Conclusion: The development and ma…

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Crossref · 10.1016/j.infsof.2020.106368

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Authors
Lwakatare, L. E., Raj, A., Crnkovic, I., Bosch, J., & Olsson, H. H.
Affiliations
  • Chalmers University of Technology (SE)
  • Malmö University (SE)
Corresponding author
Lwakatare, Lucy Ellen
Venue
Information and Software Technology
Publisher
Elsevier BV
Published
2020-11
Volume
127
Pages
106368
ISSN
0950-5849 (ISSN-L)
OpenAlex
W3039522238
Cited by
241
References
72
Citations by year
2026: 37 · 2025: 78 · 2024: 48 · 2023: 32 · 2022: 27 · 2021: 14 · 2020: 5
FWCI
18.35
Type
journal article
Language
en
Open access
closed
Locations
  • Information and Software Technology (publishedVersion)
  • Chalmers Research (Chalmers University of Technology) (submittedVersion)
Funders
VINNOVA
Topics
  • Physical Sciences › Computer Science › Information Systems › Software Engineering Research
  • Physical Sciences › Computer Science › Information Systems › Software Engineering Techniques and Practices
  • Physical Sciences › Computer Science › Artificial Intelligence › Advanced Software Engineering Methodologies
Keywords
industrial software development, software maintenance, adaptability, scalability, safety, privacy, data acquisition, model deployment, development guidelines

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