Abstract Analogy‐based estimation (ABE) is the most commonly applied method for estimating using software. Although there are several analogy‐based estimation techniques, there is no consistent conclusion of which technique is the best in all circumstances. Therefore, this article presents an evolutionary ensemble ABE (EEABE) for software cost estimation. Ensemble effort estimation (EEE) models forecast software development endeavors through using multiple estimation methods. In this article, EEABE combines GA as an evolutionary algorithm with six ABE models. The proposed method has been evaluated on four well‐known datasets comprising Maxwell, Albrecht, Kemerer, and Desharnais, by the k‐fold cross‐validation technique and based on the performance criteria of MRE, MMRE, MDMRE, BMMRE, and PRED(0.25). The simulation results demonstrate that the use of EEABE increases the accuracy of estimation and reduces the cost. Besides, EEABE is a flexible and adaptable model with any type of dataset and software development project, and it has been optimized with the possibility of using various ABE techniques.

Summary Software development effort estimation is an effective factor in the success or failure of software projects. There are several methods to estimate the effort of software projects, the most common of which is analogy‐based estimation (ABE). In this article, a polynomial version of ABE (named PABE) is presented, in which, the project effort is calculated based on a polynomial ensemble of different ABE models. To optimize the controllable parameters of the PABE model, a combined global–local search metaheuristic algorithm based on particle swarm optimization and simulated annealing is utilized in two steps. At the first step, for each similarity and adaptation function, the optimized ABE model is determined by exploiting the optimal value of feature weights, the number of similar projects, and other parameters of the ABE model. Then, at the second step, the amount of effort attained by the optimized models is used for estimating the final effort by the proposed polynomial equation. The proposed PABE method has been successfully executed on five well‐known software effort estimation datasets: Maxwell, Albrecht, Cocomo81, Desharnais, and Kemerer. Obtained results show the superiority of the proposed PABE model in terms of accuracy and efficiency compared to other techniques.

Accurate estimation of software service development effort is a great challenge both in industry and for academia. The concept of effort is an important and effective parameter in process development and software service management. The reliable estimation of effort helps the project managers to allocate the resources better and manage cost and time so that the project will be finished in the determined time and budget. One of the most popular effort estimation methods is analogy-based estimation (ABE) to compare a service with similar historical cases. Unfortunately, ABE is not capable of generating accurate results unless determining weights for service features. Therefore, this paper aims to make an efficient and reliable model through combining ABE method and differential evolution algorithm to estimate the software services development effort. In fact, the differential evolution algorithm was utilized for weighing features in the similarity function of the ABE method. This weighing process could help determining the importance level of the various service features and extracting the best similar historical case. The proposed hybrid model has been evaluated on two real datasets and two artificial datasets. The obtained results were compared with common effort estimation methods. This comparison showed more accuracy, faster convergence, and lower cost of the proposed model. Copyright © 2015 John Wiley & Sons, Ltd.