Paper Title
Clumping and ranking software cost estimation Models through multiple comparisons algorithm
Abstract
Software Cost Estimation can be described as the process of predicting the most realistic effort required to
complete a software project. Due to the strong relationship of accurate effort estimations with many crucial project
management activities, the research community has been focused on the development and application of a vast variety of
methods and models trying to improve the estimation procedure. From the diversity of methods emerged the need for
comparisons to determine the best model. However, the inconsistent results brought to light significant doubts and
uncertainty about the appropriateness of the comparison process in experimental studies. Overall, there exist several potential
sources of bias that have to be considered in order to reinforce the confidence of experiments. In this paper, we propose a
statistical framework based on a multiple comparisons algorithm in order to rank several cost estimation models, identifying
those which have significant differences in accuracy and clustering them in non-overlapping groups. The proposed
framework is applied in a large-scale setup of comparing 11 prediction models over 6 datasets. The results illustrate the
benefits and the significant information obtained through the systematic comparison of alternative methods.