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Cross- vs. within-company cost estimation studies revisited: an extended systematic review

E. Mendes,Marcos Kalinowski,Daves Martins,F. Ferrucci,Federica Sarro

Published 2014 in International Conference on Evaluation & Assessment in Software Engineering

ABSTRACT

[Objective] 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.

PUBLICATION RECORD

  • Publication year

    2014

  • Venue

    International Conference on Evaluation & Assessment in Software Engineering

  • Publication date

    2014-05-13

  • Fields of study

    Engineering, Business, Economics, Computer Science

  • Identifiers
    DOI 10.1145/2601248.2601284
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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