A Morphological-Rank-Linear Approach for Software Development Cost Estimation

This work presents a Morphological-Rank-Linear approach to solve the problem of Software Development Cost Estimation (SDCE). It consists of a hybrid morphological model, which is a linear combination between a Morphological-Rank (MR) operator (nonlinear) and a Finite Impulse Response (FIR) operator (linear), referred to as Morphological-Rank-Linear (MRL) filter. A gradient steepest descent method to adjust the MRL filter parameters (learning process), using the Least Mean Squares (LMS) algorithm, and a systematic approach to overcome the problem of nondifferentiability of the morphological-rank operator are used to improve the numerical robustness of training algorithm. Furthermore, an experimental analysis is conducted with the proposed approach using the well-known NASA database. In the experiments, two relevant performance metrics and an evaluation function are used to assess the performance of the proposed approach. The results obtained are compared to models recently presented in literature.