Lessons Learned and Results from Applying Data-Driven Cost Estimation to Industrial Data Sets
Abstract
The increasing availability of cost-relevant data in industry allows companies to apply data-intensive estimation methods. However, available data are often inconsistent, invalid, or incomplete, so that most of the existing data-intensive estimation methods cannot be applied. Only few estimation methods can deal with imperfect data to a certain extent (e.g., Optimized Set Reduction, OSR(c)). Results from evaluating these methods in practical environments are rare. This article describes a case study on the application of OSR(c) at Toshiba Information Systems (Japan) Corporation. An important result of the case study is that estimation accuracy significantly varies with the data sets used and the way of preprocessing these data. The study supports current results in the area of quantitative cost estimation and clearly illustrates typical problems. Experiences, lessons learned, and recommendations with respect to data preprocessing and data-intensive cost estimation in general are presented.
Keywords
Cite
@article{arxiv.1401.4256,
title = {Lessons Learned and Results from Applying Data-Driven Cost Estimation to Industrial Data Sets},
author = {Jens Heidrich and Adam Trendowicz and Jürgen Münch and Yasushi Ishigai and Kenji Yokoyama and Nahomi Kikuchi and T. Kawaguchi},
journal= {arXiv preprint arXiv:1401.4256},
year = {2014}
}
Comments
10 pages. The final publication is available at http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=4335245