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A taxonomy of softw...
A taxonomy of software engineering challenges for machine learning systems: An empirical investigation
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- Lwakatare, Lucy, 1987 (author)
- Chalmers tekniska högskola,Chalmers University of Technology,Department of Computer Science and Engineering, Chalmers University of Technology, Hörselgången 11, Gothenburg, 412 96, Sweden
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- Munappy, Aiswarya Raj, 1990 (author)
- Chalmers tekniska högskola,Chalmers University of Technology,Department of Computer Science and Engineering, Chalmers University of Technology, Hörselgången 11, Gothenburg, 412 96, Sweden
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- Bosch, Jan, 1967 (author)
- Chalmers tekniska högskola,Chalmers University of Technology,Department of Computer Science and Engineering, Chalmers University of Technology, Hörselgången 11, Gothenburg, 412 96, Sweden
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- Olsson Holmström, Helena (author)
- Malmö universitet,Institutionen för datavetenskap och medieteknik (DVMT)
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- Crnkovic, Ivica, 1955 (author)
- Chalmers tekniska högskola,Chalmers University of Technology,Department of Computer Science and Engineering, Chalmers University of Technology, Hörselgången 11, Gothenburg, 412 96, Sweden
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(creator_code:org_t)
- 2019-04-27
- 2019
- English.
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In: Lecture Notes in Business Information Processing. - Cham : Springer International Publishing. - 1865-1356 .- 1865-1348. ; 355, s. 227-243, s. 227-243
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Abstract
Subject headings
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- 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.
Subject headings
- NATURVETENSKAP -- Data- och informationsvetenskap -- Programvaruteknik (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Software Engineering (hsv//eng)
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik -- Datorsystem (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering -- Computer Systems (hsv//eng)
Keyword
- Machine learning
- Challenges
- Software engineering
- Artificial intelligence
Publication and Content Type
- kon (subject category)
- ref (subject category)
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