Yasushi Ishigai

Adam Trendowicz, Michael Ochs, Axel Wickenkamp et al.

Managing software development productivity and effort are key issues in software organizations. Identifying the most relevant factors influencing project performance is essential for implementing business strategies by selecting and adjusting proper improvement activities. There is, however, a large number of potential influencing factors. This paper proposes a novel approach for identifying the most relevant factors influencing software development productivity. The method elicits relevant factors by integrating data analysis and expert judgment approaches by means of a multi-criteria decision support technique. Empirical evaluation of the method in an industrial context has indicated that it delivers a different set of factors compared to individual data- and expert-based factor selection methods. Moreover, application of the integrated method significantly improves the performance of effort estimation in terms of accuracy and precision. Finally, the study did not replicate the observation of similar investigations regarding improved estimation performance on the factor sets reduced by a data-based selection method.

Jens Heidrich, Adam Trendowicz, Jürgen Münch et al.

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.