Polynomial analogy‐based software development effort estimation using combined particle swarm optimization and simulated annealing

Abstract

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.

An evolutionary ensemble analogy‐based software effort estimation

“…In Table 10, the values of MMRE and PRED(0.25) on the Kemerer dataset are compared to other popular models. Moreover, the results obtained from the EEABE are compared with other ensemble models reported in recent papers 42,49,52 . Among all models except ANFIS‐SBO, the proposed EEABE reaches the best results.…”

“…Moreover, the results obtained from the EEABE are compared with other ensemble models reported in recent papers. 42,49,52 Among all models except ANFIS-SBO, the proposed EEABE reaches the best results. The results of EEABE are not significantly different from the results of ANFIS-SBO techniques.…”

“…The outputs of EEABE and other models such as GABE, MICBR, PABE, BABE, and WGre‐ABE are provided in Table 6. GABE represents feature weighting ABE that assigns ideal feature weights via GAs; MICBR denotes CBR with mutual information; PABE denotes a polynomial ABE that using combined PSO and SA; BABE represents a model that ensemble Artificial Bee Colony with ABE; WGre‐ABE denotes a combined method integrating ABE and PSO 14,37,42,50,51 . The outputs reveal that the EEABE model is better than other models except for BABE, both in the MMRE, PRED(0.25) performance measures.…”

“…Their results suggested that the machine learning ensemble techniques perform better than solo techniques. Shahpar et al 42 proposed a polynomial analogy based estimation (PABE), in which, the effort of software project is estimated based on a polynomial ensemble of six different ABE models. They use a combination of particle swarm optimization and simulated annealing to optimize the PABE model.…”

Application of Chaotic Increasing Linear Inertia Weight and Diversity Improved Particle Swarm Optimization to Predict Accurate Software Cost Estimation

“…Hassaniet al [21] examined effects of the genetic algorithms, PSO algorithm and ant colony optimization, and presented comparative study of them. Shahpar et al [22] proposed Polynomial Analogy Based Estimation (PABE) to improve the accurate prediction of the SCE. An analogy method is used to find out similarity property of the given project.…”