Malmö University Publications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Breaking the vicious circle: A case study on why AI for software analytics and business intelligence does not take off in practice
Siemens Corp Technol, Otto Hahn Ring 6, D-81739 Munich, Germany..
Siemens Corp Technol, Otto Hahn Ring 6, D-81739 Munich, Germany..ORCID iD: 0000-0002-7537-0263
Chalmers Univ Technol, Dept Comp Sci & Engn, Horselgangen 11, S-41296 Gothenburg, Sweden..ORCID iD: 0000-0003-2854-722X
Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT).ORCID iD: 0000-0002-7700-1816
2022 (English)In: Journal of Systems and Software, ISSN 0164-1212, E-ISSN 1873-1228, Vol. 184, article id 111135Article in journal (Refereed) Published
Abstract [en]

In recent years, the application of artificial intelligence (AI) has become an integral part of a wide range of areas, including software engineering. By analyzing various data sources generated in software engineering, it can provide valuable insights into customer behavior, product performance, bugs and errors, and many more. In practice, however, AI for software analytics and business intelligence often remains at a prototypical stage, and the results are rarely used to make decisions based on data. To understand the underlying causes of this phenomenon, we conduct an explanatory case study consisting of and interview study and a survey on the challenges of realizing and utilizing artificial intelligence in the context of software-intensive businesses. As a result, we identify a vicious circle that prevents practitioners from moving from prototypical AI-based analytics to continuous and productively usable software analytics and business intelligence solutions. In order to break the vicious circle in a targeted manner, we identify a set of solutions based on existing literature as well as the previously conducted interviews and survey. Finally, these solutions are validated by a focus group of experts. (C) 2021 Elsevier Inc. All rights reserved.

Place, publisher, year, edition, pages
Elsevier, 2022. Vol. 184, article id 111135
Keywords [en]
Data analytics, Artificial intelligence, Software analytics, Business intelligence, Data-driven software engineering
National Category
Software Engineering
Identifiers
URN: urn:nbn:se:mau:diva-47264DOI: 10.1016/j.jss.2021.111135ISI: 000722219800007Scopus ID: 2-s2.0-85119991290OAI: oai:DiVA.org:mau-47264DiVA, id: diva2:1617594
Available from: 2021-12-07 Created: 2021-12-07 Last updated: 2024-02-05Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textScopus

Authority records

Olsson, Helena Holmström

Search in DiVA

By author/editor
Elsner, ChristophBosch, JanOlsson, Helena Holmström
By organisation
Department of Computer Science and Media Technology (DVMT)
In the same journal
Journal of Systems and Software
Software Engineering

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 190 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf