A Simulation Tool for Efficient Analogy Based Cost Estimation

This paper presents a statistical simulation tool, namely the bootstrap method, which helps the user in tuning the analogy approach before application to real projects, and shows how measures of accuracy and in particular, confidence intervals, may be computed for the analogy-based estimates, using thebootstrap method with different assumptions about the population distribution of the data set.

Abstract

Estimation of a software project effort, based on project analogies, is a promising method in the area of software cost estimation. Projects in a historical database, that are analogous (similar) to the project under examination, are detected, and their effort data are used to produce estimates. As in all software cost estimation approaches, important decisions must be made regarding certain parameters, in order to calibrate with local data and obtain reliable estimates. In this paper, we present a statistical simulation tool, namely the bootstrap method, which helps the user in tuning the analogy approach before application to real projects. This is an essential step of the method, because if inappropriate values for the parameters are selected in the first place, the estimate will be inevitably wrong. Additionally, we show how measures of accuracy and in particular, confidence intervals, may be computed for the analogy-based estimates, using the bootstrap method with different assumptions about the population distribution of the data set. Estimate confidence intervals are necessary in order to assess point estimate accuracy and assist risk analysis and project planning. Examples of bootstrap confidence intervals and a comparison with regression models are presented on well-known cost data sets.