Landscape of Requirements Engineering for Machine Learning-based AI Systems

Nobukazu Yoshioka*, Jati H. Husen, Hnin Thandar Tun, Zhenxiang Chen, Hironori Washizaki, Yoshiaki Fukazawa

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

Abstract

Techniques and practices in RE are not well researched, although problems and the research challenges on requirements engineering (RE) for machine learning-based systems (MLS) are evaluated via empirical case studies. A systematic literature review of RE for MLS was conducted to guide practitioners and researchers to design and research effective RE for ML systems and software. We identified 32 papers. Although many studies have been recently conducted, problem statements and research challenges remain. Future studies should include the monitoring requirements for concept drifts and how domain experts collaborate with ML experts and engineers.

Original languageEnglish
Title of host publicationProceedings - 2021 28th Asia-Pacific Software Engineering Conference Workshops, APSECW 2021
PublisherIEEE Computer Society
Pages5-8
Number of pages4
ISBN (Electronic)9781665438131
DOIs
Publication statusPublished - 2021
Event28th Asia-Pacific Software Engineering Conference Workshops, APSECW 2021 - Virtual, Online, Taiwan, Province of China
Duration: 2021 Dec 62021 Dec 9

Publication series

NameProceedings - Asia-Pacific Software Engineering Conference, APSEC
ISSN (Print)1530-1362

Conference

Conference28th Asia-Pacific Software Engineering Conference Workshops, APSECW 2021
Country/TerritoryTaiwan, Province of China
CityVirtual, Online
Period21/12/621/12/9

Keywords

  • Big-data
  • Machine Learning
  • Requirements Engineering

ASJC Scopus subject areas

  • Software

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