A differential evolution-based model to estimate the software services development effort

Abstract

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.

Enhancing Analogy-based Software Cost Estimation using Gray Wolf Optimization Algorithm

“…Therefore, they proposed a hybrid method of fuzzy clustering, ABE, and ANN, and utilized two big, real datasets to evaluate the performance of their proposed method; the results were eventually promising [43]. They also studied the software services development effort estimation in another research, to create an efficient and reliable model by combining the ABE method and the Differential Evolution (DE) algorithm [44]. Benala et al [27] studied the effectiveness of the DE algorithm for optimizing the feature weights of the ABE similarity functions using five mutation strategies.…”

CoBRA without experts: New paradigm for software development effort estimation using COCOMO metrics

“…The accuracy of ABE is not satisfactory, so many studies have tried to increase its accuracy by integrating it with different methods. Combination of ABE and PSO, 44 ABE and ANN, 45 and ABE and differential evolution‐based model 46 are other types of the ABE method. In order to achieve a high‐performance model, some studies have combined several estimation methods using specific selection methods or combination rules to estimate the effort 1,33,47–50 .…”

A Hybrid Artificial Bee Colony and Artificial Fish Swarm Algorithms for Software Cost Estimation

“…Usually, the task of SCE in companies and programming teams is the responsibility of the project manager, who of course must be an experienced project manager and have previous knowledge in these fields [8]. Estimating resources, cost, and schedule for a software engineering activity depends on things like experience, and access to relevant statistical data.…”