Hybrid PSO-SA Approach for Feature Weighting in Analogy-Based Software Project Effort Estimation

The software effort estimation plays an important role in software project management, and analogy-based estimation (ABE) is the most common method used for this purpose. ABE estimates the effort required for a new software project based on its similarity to the previous projects. A similarity between the projects is evaluated based on a set of project features, each of which has a particular effect on the degree of similarity between the projects and the effort feature. The present study examines the hybrid PSO-SA approach for feature weighting in the analogy-based software project effort estimation. The proposed approach is implemented and tested on two well-known datasets of software projects. The performance of the proposed model is compared with the other optimization algorithms based on the MMRE, MDMRE, and PRED (0.25) measures. The results obtained showed that the weighted ABE models provide more accurate and better effort estimates relative to the unweighted ABE models and that the hybrid PSO-SA approach leads to better and more accurate results compared to the other weighting approaches in both datasets.