How to Make Best Use of Cross-Company Data for Web Effort Estimation?

Leandro Minku, Federica Sarro, Emilia Mendes, Filomena Ferrucci

Research output: Chapter in Book/Report/Conference proceedingConference contribution

17 Citations (Scopus)


[Context]: The numerous challenges that can hinder software companies from gathering their own data have motivated over the past 15 years research on the use of cross-company (CC) datasets for software effort prediction. Part of this research focused on Web effort prediction, given the large increase worldwide in the development of Web applications. Some of these studies indicate that it may be possible to achieve better performance using CC models if some strategy to make the CC data more similar to the within-company (WC) data is adopted. [Goal]: This study investigates the use of a recently proposed approach called Dycom to assess to what extent Web effort predictions obtained using CC datasets are effective in relation to the predictions obtained using WC data when explicitly mapping the CC models to the WC context. [Method]: Data on 125 Web projects from eight different companies part of the Tukutuku database were used to build prediction models. We benchmarked these models against baseline models (mean and median effort) and a WC base learner that does not benefit of the mapping. We also compared Dycom against a competitive CC approach from the literature (NN-filtering). We report a company-by- company analysis. [Results]: Dycom usually managed to achieve similar or better performance than a WC model while using only half of the WC training data. These results are also an improvement over previous studies that investigated the use of different strategies to adapt CC models to the WC data for Web effort estimation. [Conclusions]: We conclude that the use of Dycom for Web effort prediction is quite promising and in general supports previous results when applying Dycom to conventional software datasets.

Original languageEnglish
Title of host publicationProceedings of the 9th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM)
Place of PublicationBeijing, China
PublisherIEEE Xplore
Number of pages10
ISBN (Electronic)9781467378994
Publication statusPublished - 5 Nov 2015
EventACM/IEEE International Symposium on Empirical Software Engineering and Measurement, ESEM 2015 - Beijing, China
Duration: 22 Oct 201523 Oct 2015


ConferenceACM/IEEE International Symposium on Empirical Software Engineering and Measurement, ESEM 2015


  • Companies
  • Databases
  • Estimation
  • Optical wavelength conversion
  • Predictive models
  • Software
  • Training

ASJC Scopus subject areas

  • Computer Science Applications
  • Software


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