Evaluation of cost estimation metrics: Towards a unified terminology

Abstract

Cost overrun of software projects is major cause of their failures. In order to facilitate accurate software cost estimation, there are several metrics, tools and datasets. In this paper, we evaluate and compare different metrics and datasets in terms of similarities and differences of involved software attributes. These metrics forecast project cost estimations based on different software attributes. Some of these metrics are public and standard while others are only employed in a particular metric tool/dataset. Sixteen public cost estimation datasets are collected and analyzed. Different perspectives are used to compare and classify those datasets. Tools for feature selection and classification are used to find the most important attributes in cost estimation datasets toward the goal of effort prediction. In order to have better estimation, it is needed to correlate cost estimation from different resources, which requires a unified standard for software cost estimation metric tools and datasets. It is pertinent that a common cost estimation model may not work for each project due to diverse project size, application areas etc. We suggest having a standardized terminology of project attributes used for cost estimation. This would improve cost estimation as multiple metrics could be applied on a project without much additional effort.