Lecture Notes in Business Information Processing, pages 227-243

A Taxonomy of Software Engineering Challenges for Machine Learning Systems: An Empirical Investigation

Lucy Ellen Lwakatare ¹

Aiswarya Raj ¹

JAN BOSCH ¹

Helena Holmström Olsson ²

Ivica Crnkovic ¹

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Department of Computer Science and Engineering, Chalmers University of Technology, Gothenburg, Sweden |

Department of Computer Science and Media Technology, Malmö University, Malmö, Sweden |

Publication type: Book Chapter

Publication date: 2019-05-10

Springer Nature

Journal: Lecture Notes in Business Information Processing

scimago Q3

SJR: 0.339

CiteScore: 2.3

Impact factor: —

ISSN: 18651348, 18651356

DOI: 10.1007/978-3-030-19034-7_14

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Abstract

Artificial intelligence enabled systems have been an inevitable part of everyday life. However, efficient software engineering principles and processes need to be considered and extended when developing AI- enabled systems. The objective of this study is to identify and classify software engineering challenges that are faced by different companies when developing software-intensive systems that incorporate machine learning components. Using case study approach, we explored the development of machine learning systems from six different companies across various domains and identified main software engineering challenges. The challenges are mapped into a proposed taxonomy that depicts the evolution of use of ML components in software-intensive system in industrial settings. Our study provides insights to software engineering community and research to guide discussions and future research into applied machine learning.

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Lwakatare L. E. et al. A Taxonomy of Software Engineering Challenges for Machine Learning Systems: An Empirical Investigation // Lecture Notes in Business Information Processing. 2019. pp. 227-243.

GOST all authors (up to 50) Copy

Lwakatare L. E., Raj A., BOSCH J., Olsson H. H., Crnkovic I. A Taxonomy of Software Engineering Challenges for Machine Learning Systems: An Empirical Investigation // Lecture Notes in Business Information Processing. 2019. pp. 227-243.

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RIS Copy

TY - GENERIC

DO - 10.1007/978-3-030-19034-7_14

UR - https://doi.org/10.1007/978-3-030-19034-7_14

TI - A Taxonomy of Software Engineering Challenges for Machine Learning Systems: An Empirical Investigation

T2 - Lecture Notes in Business Information Processing

AU - Lwakatare, Lucy Ellen

AU - Raj, Aiswarya

AU - BOSCH, JAN

AU - Olsson, Helena Holmström

AU - Crnkovic, Ivica

PY - 2019

DA - 2019/05/10

PB - Springer Nature

SP - 227-243

SN - 1865-1348

SN - 1865-1356

ER -

BibTex

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BibTex (up to 50 authors) Copy

@incollection{2019_Lwakatare,

author = {Lucy Ellen Lwakatare and Aiswarya Raj and JAN BOSCH and Helena Holmström Olsson and Ivica Crnkovic},

title = {A Taxonomy of Software Engineering Challenges for Machine Learning Systems: An Empirical Investigation},

publisher = {Springer Nature},

year = {2019},

pages = {227--243},

month = {may}

}

Found error?

Publisher

Springer Nature

Journal

Lecture Notes in Business Information Processing

scimago Q3

SJR

0.339

CiteScore

2.3

Impact factor

—

ISSN

18651348 (Print)

18651356 (Electronic)

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