Prediction accuracy measurements as a fitness function for software effort estimation

Abstract

This paper evaluates the usage of analytical programming and different fitness functions for software effort estimation. Analytical programming and differential evolution generate regression functions. These functions are evaluated by the fitness function which is part of differential evolution. The differential evolution requires a proper fitness function for effective optimization. The problem is in proper selection of the fitness function. Analytical programming and different fitness functions were tested to assess insight to this problem. Mean magnitude of relative error, prediction 25 %, mean squared error (MSE) and other metrics were as possible candidates for proper fitness function. The experimental results shows that means squared error performs best and therefore is recommended as a fitness function. Moreover, this work shows that analytical programming method is viable method for calibrating use case points method. All results were evaluated by standard approach: visual inspection and statistical significance.

Inferring Productivity Factor for Use Case Point Method

“…Due to the aims of this paper, the detailed description of well-known Use Case Points method basic principles is insignificant and hence omitted. Please refer to [4], [5] for more detailed description of the Use Case Points method. The most basic equation for Use Case Points method is equation (1).…”

Adaptive Discretization Using Golden Section to Aid Outlier Detection for Software Development Effort Estimation

“…The use of MAE has been recommended by several studies [29], [73], [24] in the past for measuring average absolute difference between actual and predicted effort. MSE has also been recommended for the SDEE field in study [74]. MSE measures the average squared loss from actual to the predicted effort.…”

Spectral Clustering Effect in Software Development Effort Estimation

“…All experiments were evaluated using the following criteria [37][38][39]: mean absolute residual (MAR), calculated by using (7), mean magnitude of the relative error (MMRE), as in (8), percentage relative error deviation (PRED), as in (9), and mean absolute percentage error (MAPE), as in (10). Finally, the sum of squared errors (11) and mean squared error (MSE), as in (12), were included in the evaluation.…”