How to treat timing information for software effort estimation?

Abstract

Software development effort estimation is an essential aspect of software project management. An effort estimation model expresses relationships between effort and factors such as organizational and project features (e.g. software functional size, and the programming language used in a project). However, software development practices and tools change over time, to environmental changes. This can affect some relationships assumed in an effort estimation model. A moving windows method (a method for treating the timing information of projects), has thus been proposed for estimation models. The moving windows method uses data from a fixed number of the most recent projects data for model construction. However, it is not clear that moving windows is the best way to handle the timing information in an estimation model. The goal of our research is to determine how best to treat timing information in constructing effort estimation models. To achieve the goal, we compared six different methods (moving windows, dummy variable of moving windows, dummy variables of equal bins, dummy variables of year, year predictor, and serial number) for treating timing data, in terms of estimation accuracy. In the experiment, we use three software development project datasets. We found that moving windows is best when the number of projects included in the dataset is not small, and dummy variable of moving windows is the best when the number is small.