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
Machine learning for software analysis: Models, methods, and applications
2018 (English)In: International Dagstuhl Seminar 16172 Machine Learning for Dynamic Software Analysis: Potentials and Limits, 2016, Springer, 2018, Vol. 11026, p. 3-49Conference 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 including: behaviour extraction, testing and bug fixing. Many more applications are yet to be defined. Therefore, a better fundamental understanding of ML methods, their assumptions and guarantees can help to identify and adopt appropriate ML technology for new applications. In this chapter, we present an introductory survey of ML applications in software engineering, classified in terms of the models they produce and the learning methods they use. We argue that the optimal choice of an ML method for a particular application should be guided by the type of models one seeks to infer. We describe some important 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
Springer, 2018. Vol. 11026, p. 3-49
Series
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), ISSN 0302-9743 ; 11026
Keywords [en]
Machine learning, Software engineering
National Category
Other Computer and Information Science
Identifiers
URN: urn:nbn:se:kth:diva-233742DOI: 10.1007/978-3-319-96562-8_1ISI: 000476941200001Scopus ID: 2-s2.0-85051142497ISBN: 9783319965611 (print)OAI: oai:DiVA.org:kth-233742DiVA, id: diva2:1243394
Conference
International Dagstuhl Seminar 16172 Machine Learning for Dynamic Software Analysis: Potentials and Limits, 2016; Wadern; Germany; 24 April 2016 through 27 April 2016
Funder
Vinnova, 2013-05608 VIRTUESEU, European Research Council, 291652 (ASAP), and the EPSRC EP/R013144/1 SAUSE project
Note

QC 20180831

Available from: 2018-08-31 Created: 2018-08-31 Last updated: 2019-08-12Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textScopus

Search in DiVA

By author/editor
Meinke, Karl
By organisation
KTH
Other Computer and Information Science

Search outside of DiVA

GoogleGoogle Scholar

doi
isbn
urn-nbn

Altmetric score

doi
isbn
urn-nbn
Total: 196 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