A taxonomy of software engineering challenges for machine learning systems: An empirical investigation
Paper i proceeding, 2019

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.

Machine learning

Challenges

Software engineering

Artificial intelligence

Författare

Lucy Lwakatare

Chalmers, Data- och informationsteknik, Software Engineering, Software Engineering for People, Architecture, Requirements and Traceability

Aiswarya Raj Munappy

Chalmers, Data- och informationsteknik, Software Engineering, Software Engineering for Cyber Physical Systems

Jan Bosch

Chalmers, Data- och informationsteknik, Software Engineering, Software Engineering for Cyber Physical Systems

Helena Holmström Olsson

Malmö universitet

Ivica Crnkovic

Chalmers, Data- och informationsteknik, Software Engineering, Software Engineering for People, Architecture, Requirements and Traceability

Lecture Notes in Business Information Processing

1865-1348 (ISSN)

Vol. 355 227-243

20th International Conference on Agile Software Development, XP 2019
Montreal, Canada,

Ämneskategorier

Programvaruteknik

Datavetenskap (datalogi)

Datorsystem

DOI

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

Mer information

Senast uppdaterat

2019-11-13