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Using ensembles for web effort estimation
Blekinge Institute of Technology, School of Computing.
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2013 (English)Conference paper, Published paper (Refereed) Published
Abstract [en]

Background: Despite the number of Web effort estimation techniques investigated, there is no consensus as to which technique produces the most accurate estimates, an issue shared by effort estimation in the general software estimation domain. A previous study in this domain has shown that using ensembles of estimation techniques can be used to address this issue. Aim: The aim of this paper is to investigate whether ensembles of effort estimation techniques will be similarly successful when used on Web project data. Method: The previous study built ensembles using solo effort estimation techniques that were deemed superior. In order to identify these superior techniques two approaches were investigated: The first involved replicating the methodology used in the previous study, while the second approach used the Scott-Knott algorithm. Both approaches were done using the same 90 solo estimation techniques on Web project data from the Tukutuku dataset. The replication identified 16 solo techniques that were deemed superior and were used to build 15 ensembles, while the Scott-Knott algorithm identified 19 superior solo techniques that were used to build two ensembles. Results: The ensembles produced by both approaches performed very well against solo effort estimation techniques. With the replication, the top 12 techniques were all ensembles, with the remaining 3 ensembles falling within the top 17 techniques. These 15 effort estimation ensembles, along with the 2 built by the second approach, were grouped into the best cluster of effort estimation techniques by the Scott-Knott algorithm. Conclusion: While it may not be possible to identify a single best technique, the results suggest that ensembles of estimation techniques consistently perform well even when using Web project data

Place, publisher, year, edition, pages
Baltimore: IEEE , 2013.
Keyword [en]
ensembles, replication, Scott-Knott algorithm, Web effort estimation
National Category
Software Engineering
Identifiers
URN: urn:nbn:se:bth-6729DOI: 10.1109/ESEM.2013.25Local ID: oai:bth.se:forskinfoCBF17F3703F2269FC1257CBB00340C12OAI: oai:DiVA.org:bth-6729DiVA: diva2:834262
Conference
International Symposium on Empirical Software Engineering and Measurement(ESEM)
Available from: 2014-04-15 Created: 2014-04-15 Last updated: 2015-06-30Bibliographically approved

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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