Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • 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
Machine learning for software engineering: Models, methods, and applications
KTH, School of Electrical Engineering and Computer Science (EECS), Theoretical Computer Science, TCS.ORCID iD: 0000-0002-9706-5008
2018 (English)In: Proceedings of the 40th International Conference on Software Engineering: Companion Proceeedings, IEEE Computer Society, 2018, p. 548-549Conference paper, Published paper (Refereed)
Abstract [en]

Machine Learning (ML) is the discipline that studies methods for automatically inferring models from data. Machine learning has been successfully applied in many areas of software engineering ranging from behaviour extraction, to testing, to bug fixing. Many more applications are yet be defined. However, a better understanding of ML methods, their assumptions and guarantees would help software engineers adopt and identify the appropriate methods for their desired applications. We argue that this choice can be guided by the models one seeks to infer. In this technical briefing, we review and reflect on the applications of ML for software engineering organised according to the models they produce and the methods they use. We introduce the principles of ML, give an overview of some key methods, and present examples of areas of software engineering benefiting from ML. We also discuss the open challenges for reaching the full potential of ML for software engineering and how ML can benefit from software engineering methods.

Place, publisher, year, edition, pages
IEEE Computer Society, 2018. p. 548-549
Series
Proceedings - International Conference on Software Engineering, ISSN 0270-5257
National Category
Software Engineering
Identifiers
URN: urn:nbn:se:kth:diva-238226DOI: 10.1145/3183440.3183461ISI: 000450109000227Scopus ID: 2-s2.0-85049687459ISBN: 9781450356633 (print)OAI: oai:DiVA.org:kth-238226DiVA, id: diva2:1263063
Conference
40th ACM/IEEE International Conference on Software Engineering, ICSE 2018, 27 May 2018 through 3 June 2018
Note

QC 20181114

Available from: 2018-11-14 Created: 2018-11-14 Last updated: 2018-12-04Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textScopus

Authority records BETA

Meinke, Karl

Search in DiVA

By author/editor
Meinke, Karl
By organisation
Theoretical Computer Science, TCS
Software Engineering

Search outside of DiVA

GoogleGoogle Scholar

doi
isbn
urn-nbn

Altmetric score

doi
isbn
urn-nbn
Total: 107 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • 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