Propose-Specific Information Related to Prediction Level at x and Mean Magnitude of Relative Error: A Case Study of Software Effort Estimation

Abstract

The prediction level at x ((Formula presented.)) and mean magnitude of relative error ((Formula presented.)) are measured based on the magnitude of relative error between real and predicted values. They are the standard metrics that evaluate accurate effort estimates. However, these values might not reveal the magnitude of over-/under-estimation. This study aims to define additional information associated with the (Formula presented.) and (Formula presented.) to help practitioners better interpret those values. We propose the formulas associated with the (Formula presented.) and (Formula presented.) to express the level of scatters of predictive values versus actual values on the left ((Formula presented.)), on the right ((Formula presented.)), and on the mean of the scatters ((Formula presented.)). We depict the benefit of the formulas with three use case points datasets. The proposed formulas might contribute to enriching the value of the (Formula presented.) and (Formula presented.) in validating the effort estimation.