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Cross- vs. Within-company cost estimation studies revisited: An extended systematic review
Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
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2014 (English)Conference paper (Refereed) Published
Abstract [en]

The objective of this paper is to extend a previously conducted systematic literature review (SLR) that investigated under what circumstances individual organizations would be able to rely on cross-company based estimation models. [Method] We applied the same methodology used in the SLR we are extending herein (covering the period 2006-2013) based on primary studies that compared predictions from cross-company models with predictions from within-company models constructed from analysis of project data. [Results] We identified 11 additional papers; however two of these did not present independent results and one had inconclusive findings. Two of the remaining eight papers presented both, trials where cross-company predictions were not significantly different from within-company predictions and others where they were significantly different. Four found that cross-company models gave prediction accuracy significantly different from within-company models (one of them in favor of cross-company models), while two found no significant difference. The main pattern when examining the study related factors was that studies where cross-company predictions were significantly different from within-company predictions employed larger within-company data sets. [Conclusions] Overall, half of the analyzed evidence indicated that cross-company estimation models are not significantly worse than within-company estimation models. Moreover, there is some evidence that sample size does not imply in higher estimation accuracy, and that samples for building estimation models should be carefully selected/filtered based on quality control and project similarity aspects. The results need to be combined with the findings from the SLR we are extending to allow further investigating this topic.

Place, publisher, year, edition, pages
London: ACM , 2014.
Keyword [en]
Cost estimation models, Cross-company data, Estimation accuracy, Systematic review, Within-company data
National Category
Software Engineering
Identifiers
URN: urn:nbn:se:bth-6466DOI: 10.1145/2601248.2601284Local ID: oai:bth.se:forskinfoEECDC21C14E1DB0DC1257DBE0032DBA9ISBN: 978-145032476-2 (print)OAI: oai:DiVA.org:bth-6466DiVA: diva2:833974
Conference
International Conference on Evaluation and Assessment in Software Engineering (EASE)
Available from: 2014-12-30 Created: 2014-12-30 Last updated: 2015-06-30Bibliographically approved

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CiteExportLink to record
Permanent link

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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
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