Software effort estimation using ensemble of hybrid search-based algorithms based on metaheuristic algorithms

Abstract

Software effort estimation is an essential task for software organizations to allocate resources efficiently during the development of software and to negotiate with customers. Various machine learning techniques-based models are used to predict the efforts required for the development of software products. These models utilize the past data of software projects to predict the efforts. In the present work, software efforts are estimated with the weighted ensemble of hybrid search-based algorithms. Weighted ensembles are created with the help of metaheuristic algorithms like firefly algorithm, black hole optimization, and genetic algorithm. The weighted ensemble of hybrid search-based algorithms based on metaheuristics algorithms is compared with some well-known machine learning algorithms and machine learning-based ensemble techniques. All the experiments were performed on three datasets obtained from PROMISE repository. Experiments were performed in R programming language using RKEEL and MetaheuristicsOpt r packages. Obtained results showed that proposed metaheuristics algorithms-based weighted ensembles of hybrid search-based algorithms are good and outperformed machine learning-based algorithms and their ensembles for prediction of software effort.